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The growth of them follows the logistic growth function. Both the species release toxic substances which are harmful to each other. The boundedness, persistence, equilibria, stability, bionomic equilibrium and optimal harvesting policy have been studied. We have shown that the dynamical outcomes of the interacting fish species will much sensitive to the system parameters and their initial population volumes. Counter-intuitive results on role played by toxic coefficients are highly gated. 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KJ, Kurz M (1970) Public investment, the rate of return and optimal fiscal policy. John Hopkins, Baltimore",{"id":22,"text":281,"url":22,"identifiers":22},"Birkhoff G, Rota GC (1982) Ordinary differential equations. Ginn Boston",{"id":22,"text":283,"url":22,"identifiers":22},"Chattopadhyay J (1996) Effect of toxic substances on a two species competitive system. Ecol Model 84:287–289",{"id":22,"text":285,"url":22,"identifiers":22},"Clark CW (1976) Mathematical bioeconomics: the optimal management of renewable resources. Wiley, New York",{"id":22,"text":287,"url":22,"identifiers":22},"De Lunna J, Hallam TG (1987) Effect of toxicants on population: a qualitative approach iv. Resource-consumer-toxicant models. Ecol Model 35:249–273",{"id":22,"text":289,"url":22,"identifiers":22},"Dubey B, Hossain J (2000) A model for the allelopathic effect on two competing species. Ecol Model 129:195–207",{"id":22,"text":291,"url":22,"identifiers":22},"Flaaten O (1998) On the bioeconomics of predator prey fishery. Fish Res 37:179–191",{"id":22,"text":293,"url":22,"identifiers":22},"Freedman HI, Shukla JB (1900) Models for the effect of toxicant in a single species and predator prey systems. J Math Biol 30:15–30",{"id":22,"text":295,"url":22,"identifiers":22},"Hale J (1989) Ordinary differential equation. Klieger Publishing Company, Malabar",{"id":22,"text":297,"url":22,"identifiers":22},"Hallam TG, Clark CW (1982) Non-autonomous logistic equations as models of populations in deteriorating environment. J Theor Biol 93:303–311",{"id":22,"text":299,"url":22,"identifiers":22},"Hallam TG, De Lunna TJ (1984) Effects of toxicants on populations: a qualitative approach III. Environmental and food chain pathways. J Theor Biol 109:411–429",{"id":22,"text":301,"url":22,"identifiers":22},"Kar TK, Chudhuri KS (2003) On non-selective harvesting of two competing fish species in the presence of toxicity. Ecol Model 161:125–137",{"id":22,"text":303,"url":22,"identifiers":22},"Kot M (2001) Elements of mathematical. Ecology 50:205–207",{"id":22,"text":305,"url":22,"identifiers":22},"Lotka AJ (1925) Elements of physical biology. Williams and Wilkins, Baltimore",{"id":22,"text":307,"url":22,"identifiers":22},"Mesterton-Gibbons M (1988) On the optimal policy for the combined harvesting of predator and prey. Nat Res Model 3:63–90",{"id":22,"text":309,"url":22,"identifiers":22},"Mesterton-Gibbons M (1996) A technique for finding optimal two-species harvesting policies. Ecol Model 92:235–244",{"id":22,"text":311,"url":22,"identifiers":22},"Mukhopadhyay A, Chattopadhyay J, Taposwi PK (1998) A delay differiential equations model of plankton allelopathy. Math Biosci 149:167–189",{"id":22,"text":313,"url":22,"identifiers":22},"Pontryagin LS, Boltyanskii VS, Gamkrelidze RV, Mishchencko EF (1962) The mathematical theory of optimal processes. Wiley, New York",{"id":22,"text":315,"url":22,"identifiers":22},"Shukla JB, Dubey B (1996) Simulteneous effects of two toxicants on biological species: a mathematical model. J Biol Syst 4:109–130",{"id":22,"text":317,"url":22,"identifiers":22},"Smith JM (1974) Models in ecology. xii, 146. University Press, New York",{"id":22,"text":319,"url":22,"identifiers":22},"Solow RM (1974) The economics of resources or the resources of economics. Am Econ Rev 64:1–14",{"id":22,"text":321,"url":22,"identifiers":22},"Volterra V (1926) Variazioni e uttuazioni del numero d’individui in specie animali conviventi. Memoria della Reale Accademia Nazionale dei Lincei II I(6):31–113 (in Italian)",false,{"id":324,"createTime":325,"updateTime":326,"relativeEntities":327,"slug":328,"properties":329,"entityType":172,"verifyStatus":173,"verifyTime":340,"verifyNote":175,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":341,"fullTextUrl":22,"authors":342,"publicationType":215,"publisherRelationship":386,"citationCount":23,"citationInfo":445,"publishDate":448,"publishYear":446,"citationAnalyzeStatus":274,"lastCitationAnalyze":449,"indexDatabases":450,"openAccess":22,"references":22,"isForceReanalyzing":322},"a05c8e30-d926-4dcb-8088-02b4845bc7c3","2024-02-06T12:39:51.117+00:00","2026-08-16T18:06:16.713+00:00",[],"Calibration-the-area-reduction-method-in-sediment-distribution-of-Ekbatan-reservoir-dam-using-genetic-algorithms",{"abstract":330,"title":332,"gsPaper":334,"references":336,"doi":338},{"EN":331},"Dam reservoirs usually play the most important role in the water resources systems and their optimal utilization in economic and social terms is indispensable. Sedimentation in dam’s reservoirs is one of the destructive phenomena which leads to reduction of useful volume of reservoirs and also damages the installations and disturbs their functions. Area reduction method is the most common experimental method to measure the sediment distribution in reservoirs. In this method, reservoirs are geometrically divided into four types. Parameters obtained for each type are based on limited number of chosen reservoirs and consequently the results lead to large scale errors for accuracy of this method. Therefore choosing appropriate parameters can help us to have more acceptable accuracy. In this study, first based on area reduction method a model was made by using MATLAB software and optimized by GA. Error declined by 46.7 %. Then elevation–area–capacity curves for following years were predicted by best coefficients.",{"EN":333},"Calibration the area-reduction method in sediment distribution of Ekbatan reservoir dam using genetic algorithms",{"VOID":335},"[\"267377685271088407\"]",{"VOID":337},"Annandale GW (1984) Predicting the distribution of deposited sediment in Southern African Reservoir. Nat Hydrol Symp 144:549–557\nAnnandale GW (1987) Development in water science, reservoir sedimentation (1st ed). BV Rand Afrikaans University: Elsevier Science Publishers\nAntoniou A, Lu WS (2007) Practical optimization algorithms and engineering applications. Springer Science + Business Media LLC, New York\nBlanton III JO, Ferrari RL (1992) Lake Texana 1991 Sedimentation Survey. Bureau of Reclamation, Technical Service Center, Denver, Colorado\nBorland WM, Miller CR (1958) Distribution of sedimentation in large Reservoirs. J Hydraul Div ASCE HY2\nBreierava L, Choudhari M (2001) An introduction to sensitivity analysis. Massachusetts Institute of Technology, Cambridge\nChambers L (2001) The practical handbook of genetic algorithms applications. Chapman & Hall, London\nDavis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, NewYork\nEmadi AR, Khademi M, Mohamadiha A (2012) Application of simulated annealing algorithm in calibration of area reduction method in sediment distribution of dams reservoir (case study: Karaj Dam). J Water Soil Conserv 19:173–188\nEngelbrecht AP (2002) Computational intelligence an introduction. Wiley, New York\nFerrari RL (1998) Prineville Reservoir 1998 Sedimentation Survey. Bureau of Reclamation. Technical Service Center, Denver, Colorado\nFerrari RL (2008) Altus Reservoir 2007 Sedimentation Survey. Bureau of Reclamation. Technical Service Center, Denver\nGharaghezlou M, Masoudian M, Fendereski R (2014) Calibrating the experimental area reduction method in assessing the distribution of sediments in Droodzan Reservoir Dam in Iran. J Civ Eng Urban 4:54–58\nGill MA (1979) Sedimentation and useful life of reservoirs. J Hydrol 44:89–95\nGoldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, MA\nHaupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New Jersey\nHolland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor\nKia SM (2009) Genetic algorithms in Matlab. Kian Rayaneh Sabz, Tehran\nLara JM (1971) The 1967 Altus Reservoir Sediment Survey. United States Department of the Interior, Hydrology Branch, Denver, Colorado\nMcCall J (2005) Genetic algorithms for modelling and optimisation. J Comput Appl Math 184:205–222\nMitchell M (1998) An introduction to genetic algorithms. MIT Press, Cambridge\nMohammadiha A, Emadi A, Mohammad Vali Samani J (2010) Auto calibration of area-reduction method in sediment distribution of dam reservoir using genetic algorithm. Iran J Soil Water Res 25:356–364\nMohammadzadeh Habili J, Mousavi F (2008) Improvement of the reservoir shape factor method and evaluation of its changes due to sedimentation. J Water Soil 22:407–416\nMousavi SF, Haydarpour M, Shabanlou S (2006) Investigation of sediments in the Zayandehrud reservoir through area-increment and area-reduction empirical models. Water Wastewater 17:76–82\nPelikan M, Goldberg DE, Lobo FG (1999) A survey of optimization by building and using probabilistic models. University of Illinois Genetic Algorithms Laboratory, Urbana\nPierre YJ (2002) River mechanics. Cambridge University Press, New York\nRao SS (2009) Engineering optimization, theory and practice, 4th edn. Wiley, New Jersey\nShafai Bajestan M (2011) Hydraulics of sediment transport, 2nd edn. Shahid Chamran University Press, Ahwaz\nShafiee AH, Safamehr M (2011) Study of sediments water resources system of Zayanderud Dam through area increment and area reduction methods. Proc Earth Planet Sci 4:29–38\nStrand RI, Pemberton EL (1982) Reservoir sedimentation. US Bureau of Reclamation, Denver\nUnited States Bureau of Reclamation (1962) Revision of the procedure to compute sediment distribution in large reservoirs. Sedimentation Section, Hydrology Branch\nVose MD (1999) The simple genetic algorithm. MIT Press, Cambridge\nWu W (2007) Computational river dynamics. National Center for Computational Hydroscience and Engineering, University of Mississippi, MS, USA\nJain SK (2003) Water resources systems planning and management. Elsevier Science, London\nYang CT (1996) Sediment transport: theory and practice. 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study tried to investigate the effect of Co\u002FSiO2 NPs on CO2 absorption in a single raising bubble column (20 °C and 1 atm). Co-doped SiO2 nanoparticles were first synthesized through the chemical vapor deposition (CVD) method, then several nanofluids, including different weight percentages of the synthesized NPs (0.001, 0.01, 0.02, 0.05, and 0.1 wt%) were prepared. Comprehensive experimental studies examined the effect of NPs concentration and nanofluid volume on CO2 absorption rate. The stability of nanofluids, as an affecting factor on nanofluid efficiency, was investigated over 10 days. It was tried to obtain mass transfer parameters, including Sherwood (Sh), and Schmidt (Sc) numbers, incorporating the CO2 diffusivity into the Co\u002FSiO2 nanofluid. Results showed that increasing NPs concentration from 0.001 to 0.02 caused the CO2 absorption rate to reach a maximum point followed by a downward trend. Increasing nanofluid volume was not beneficial for increasing gas absorption, which is attributed to the fact that the predominant mechanism of CO2 absorption was the Brownian motion of NPs. Results confirmed that the prepared nanofluids had acceptable stability over 10 days, and the nanofluid (80 mL), including 0.02 wt% of NPs, had the maximum CO2 absorption, which was 28% more than the base fluid. Findings indicated that the magnitude of the CO2 mass transfer coefficient in the nanofluid was 1.953 * 10− 4 (m.s− 1), which was 1.89 times more than that for the base fluid. Finally, a comprehensive correlation (R2 = 0.99) was introduced to predict the CO2 mass transfer coefficient in the Co\u002FSiO2 nanofluid.",{"EN":461},"Experimental and modeling of CO2 absorption in a bubble column using a water-based nanofluid containing co-doped SiO2 nanoparticles",{"VOID":463},"[\"5028738095443829282\"]",{"VOID":465},"Åhlén M, Zhou Y, Hedbom D, Cho HS, Strømme M, Terasaki O, and Ocean Cheung (2023). 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Chem Eng J 96(1–3):23–27",{"VOID":467},"10.1007\u002Fs40808-023-01869-1","2024-08-30T23:19:42.970+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40808-023-01869-1",[471,504,525,542],{"id":472,"sortIndex":23,"researcher":22,"roles":473,"affiliations":474,"properties":499,"displayName":501,"givenName":22,"familyName":22},"26154d24-f07e-4816-99e3-81239b644455",[346],[475,483,491],{"id":476,"sortIndex":23,"affiliation":477,"properties":22},"25c9ab05-ae62-423a-81fd-1635723b7a9d",{"id":476,"createTime":22,"updateTime":22,"relativeEntities":478,"slug":22,"properties":479,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":482,"statistic":22},[],{"title":480},{"VI":481},"Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, 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Haddad",{"url":469,"publisher":557,"properties":610},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":558,"slug":10,"properties":559,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":563,"manageAffiliations":579,"indexDatabases":590,"url":22,"thumbnailPath":22,"statistic":605,"gsStatistic":22,"type":150,"analyzePriority":22},[],{"issn":560,"title":561,"eissn":562},{"VOID":15},{"EN":17},{"VOID":13},[564,567,571,575],{"id":26,"createTime":22,"updateTime":22,"relativeEntities":565,"label":566,"description":22,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":29},{"id":31,"createTime":22,"updateTime":22,"relativeEntities":568,"label":569,"description":570,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":34},{},{"id":37,"createTime":22,"updateTime":22,"relativeEntities":572,"label":573,"description":574,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":40},{},{"id":43,"createTime":22,"updateTime":22,"relativeEntities":576,"label":577,"description":578,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":46},{},[580,585],{"id":50,"createTime":22,"updateTime":22,"relativeEntities":581,"slug":22,"properties":582,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":584,"statistic":22},[],{"title":583},{"EN":54},[56],{"id":58,"createTime":22,"updateTime":22,"relativeEntities":586,"slug":22,"properties":587,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":589,"statistic":22},[],{"title":588},{"EN":62},[56],[591,598],{"id":66,"indexDatabase":592,"url":79,"indexYears":22,"academicFieldIds":597,"indexDatabaseRanking":22},{"id":68,"createTime":22,"updateTime":22,"relativeEntities":593,"label":594,"description":595,"key":75,"publicationTags":596,"standard":22},[],{"EN":71,"VI":71},{"EN":73,"VI":74},[77,78],[81],{"id":83,"indexDatabase":599,"url":94,"indexYears":95,"academicFieldIds":604,"indexDatabaseRanking":101},{"id":85,"createTime":22,"updateTime":22,"relativeEntities":600,"label":601,"description":602,"key":91,"publicationTags":603,"standard":22},[],{"EN":88,"VI":88},{"EN":88,"VI":90},[93],[97,98,99,100],{"impactFactor":23,"impactFactorByYear":606,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":607,"totalCitation":126,"totalCitationByYear":608,"totalCitationPerPublication":138,"totalCitationPerPublicationByYear":609,"hindexLast5Year":149,"hindex":149},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":120,"2020":121,"2021":122,"2022":123,"2023":124,"2024":125},{"2015":128,"2016":129,"2017":130,"2018":131,"2019":132,"2020":133,"2021":134,"2022":135,"2023":136,"2024":137},{"2015":140,"2016":141,"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147,"2023":148,"2024":104},{"pages":611},{"VOID":612},"1-13",{"total":23,"publishYear":614,"statisticByYear":615},2024,{},"2024-02-16","DONE_ANALYZE_CITATION",[101,77],{"id":620,"createTime":621,"updateTime":622,"relativeEntities":623,"slug":624,"properties":625,"entityType":172,"verifyStatus":173,"verifyTime":636,"verifyNote":175,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":637,"fullTextUrl":22,"authors":638,"publicationType":215,"publisherRelationship":688,"citationCount":747,"citationInfo":748,"publishDate":754,"publishYear":749,"citationAnalyzeStatus":617,"lastCitationAnalyze":755,"indexDatabases":756,"openAccess":22,"references":22,"isForceReanalyzing":322},"23e5572e-aa83-43e3-808c-e194e48bc4d6","2024-01-30T18:05:59.305+00:00","2026-07-26T06:09:53.044+00:00",[],"Ecological-carrying-capacity-of-public-green-spaces-as-a-sustainability-index-of-urban-population-a-case-study-of-Mashhad-city-in-Iran",{"abstract":626,"title":628,"gsPaper":630,"references":632,"doi":634},{"EN":627},"Urban planners usually define carrying capacity as the ability of a natural zone or a built area to absorb population or development without any risk for both zone and population. This research aims to expand a framework for investigation of urban carrying capacity, which can determine a sustainability index of urban population based on public green spaces. For this purpose, a conventional three-level procedure of ecological carrying capacity for public green spaces was considered in Mashhad city, Iran. In this regard, the physical, real and effective carrying capacities were estimated for total public green spaces in Mashhad as about 68, 22 and 44% from total population, respectively. Furthermore, among Mashhad municipality districts, two districts represented the highest values of carrying capacity and high classes of ecological carrying capacity index over than 1 in Mashhad city due to accessibility to the natural patches and man-made gardens.",{"EN":629},"Ecological carrying capacity of public green spaces as a sustainability index of urban population: a case study of Mashhad city in Iran",{"VOID":631},"[\"1399452888432733074\"]",{"VOID":633},"Beatley T (2000) Green urbanism: learning from European cities. Island Press, Washington, DC\nBernadette OR, Morrissey J, Foley W, Moles R (2009) The relationship between settlement population size and sustainable development measured by two sustainability metrics. Environ Impact Assess Rev 29(3):169–178\nCeballes-Lascurain H (1996). Tourism, ecotourism and protected areas: the state of nature-based tourism around the world and guidelines for its development. Gland and Cambridge\nChiesura A (2004) The role of urban parks for the sustainable city. Landsc Urban Plan 68:129–138\nCifuentes MA (1992) Determinacion de capacidade de carga turística em áreas protegidas. Biblioteca Orton IICA\u002FCATIE, Costa Rica\nCifuentes MA, Mesquita CAB, Méndez J, Morales ME, Aguilar N, Cancino D (1999) Capacidad de carga turística de las áreas de Uso Público del Monumento Nacional Guayabo, Costa Rica. WWF Centro America, Costa Rica\nDong L, Zhiming F, Yanzhao Y, Zhen Y (2011) Spatial patterns of ecological carrying capacity supply–demand balance in China at county level. J Geogr Sci 21(5):833–844\nFarrell TA, Marion JL (2002) The protected area visitor impact management (PAVIM) framework: a simplified process for making management decisions. J Sustain Tour 10(1):31–51\nFinco A, Nijkamp P (2003) Pathways to urban sustainability. J Environ Policy Plan 3:289–302\nGeological survey of Iran (2010) Geological sheet of k-4, Scale 1:250,000. http:\u002F\u002Fwww.gsi.ir. Accessed 2010\nGraymore MLM, Sipe NG, Rickson RE (2008) Regional sustainability: how useful are current tools of sustainability assessment at the regional scale? Ecol Econ 67:362–372\nGraymore MLM, Sipe NG, Rickson RE (2010) Sustaining human carrying capacity: a tool for regional sustainability assessment. Ecol Econ 69(3):459–468\nHerold M, Goldstein NC, Clarke KC (2003) The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302\nHuang Q, Wang RH, Ren ZY, Li J, Zhang H (2007) Regional ecological security assessment based on long periods of ecological footprint analysis. Resour Conserv Recycl 51(1):24–41\nIranian Meteorological Organization (2015) Climatic data of Mashhad Synoptic Station (1951–2015). http:\u002F\u002Fwww.irimo.ir. Accessed 2015\nJiang D, Chen Z, Dai G (2017) Evaluation of the carrying capacity of marine industrial parks: a case study in China. Mar Policy 77:111–119\nJim CY (2004) Green-space preservation and allocation for sustainable greening of compact cities. Cities 21(4):311–320\nKong F, Yin H, Nakagoshi N (2007) Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: a case study in Jinan City, China. Landsc Urban Plan 79:240–252\nLiu RZ, Borthwick AGL (2011) Measurement and assessment of carrying capacity of the environment in Ningbo, China. J Environ Manag 92(8):2047–2053\nMansouri Daneshvar MR, Rezayi S, Khosravi S (2013) Earthquake vulnerability zonation of Mashhad urban fabric by combining the quantitative models in GIS, northeast of Iran. Int J Environ Prot Policy 1(4):44–49\nNASA (2011) The advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM). National Aeronautics and Space Administration (NASA). Earth Remote Sensing Data Analysis Center (ERSDAC)\nOh K, Jeong Y, Lee D, Lee W, Choi J (2005) Determining development density using the urban carrying capacity assessment system. Landsc Urban Plan 73(1):1–15\nPrato T (2009) Fuzzy adaptive management of social and ecological carrying capacities for protected areas. J Environ Manag 90:2551–2557\nQueiroz RE, Ventura MA, Guerreiro JA, da Cunha RT (2014) Carrying capacity of hiking trails in Natura 2000 sites: a case study from North Atlantic Islands (Azores, Portugal). J Integr Coast Zone Manag 14(2):233–242\nRess WE (1992) Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environ Urban 4(2):121–130\nRodella I, Corbau C, Simeoni U, Utizi K (2017) Assessment of the relationship between geomorphological evolution, carrying capacity and users’ perception: case studies in Emilia–Romagna (Italy). Tour Manag 59:7–22\nRoy S, Byrne J, Pickering C (2012) A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For Urban Green 4(11):351–363\nSaveriades A (2000) Establishing the social carrying capacity for tourism resorts of east coast of Republic of Cyprus. Tour Manag 21(2):147–156\nSayan S, Ortaçeşme V (2006) Recreational carrying capacity assessment in a Turkish national park. In: Proceedings of the third international conference on monitoring and management of visitor flows in recreational and protected areas, Rapperswil, Switzerland. pp 211–216\nStatistical Centre of Iran (2011) Macro results of statistical survey. http:\u002F\u002Fwww.amar.org.ir. Accessed 2011\nWagar JA (1964) The carrying capacity of wild lands for recreation. Report. Society of American Foresters, Washington, DC\nWei Y, Huang C, Lam P, Sha Y, Feng Y (2015) Using urban-carrying capacity as a benchmark for sustainable urban development: an empirical study of Beijing. Sustainability 7:3244–3268\nWolch JR, Byrne J, Newell JP (2014) Urban green space, public health, and environmental justice: the challenge of making cities ‘just green enough’. Landsc Urban Plan 125:234–244\nXu L, Kang P, Wei J (2010) Evaluation of urban ecological carrying capacity: a case study of Beijing, China. Procedia Environ Sci 2:1873–1880\nYishao S, Hefeng W, Changying Y (2013). Evaluation method of urban land population carrying capacity based on GIS—a case of Shanghai, China. Comput Environ Urban Syst 39:27–38\nYue TX, Tian YZ, Liu JY, Fan ZM (2008) Surface modeling of human carrying capacity of terrestrial ecosystems in China. Ecol Model 214:168–180\nZacarias DA, Williams AT, Newton A (2011) Recreation carrying capacity estimations to support beach management at Praia de Faro, Portugal. Appl Geogr 31:1075–1081",{"VOID":635},"10.1007\u002Fs40808-017-0364-2","2024-05-10T13:58:33.065+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40808-017-0364-2",[639,656,673],{"id":640,"sortIndex":23,"researcher":22,"roles":641,"affiliations":642,"properties":651,"displayName":653,"givenName":22,"familyName":22},"5890b3f5-7d98-4dc1-9036-b140a90edddb",[346],[643],{"id":644,"sortIndex":23,"affiliation":645,"properties":22},"a100c06b-410c-4161-b402-9a7db6450ed3",{"id":644,"createTime":22,"updateTime":22,"relativeEntities":646,"slug":22,"properties":647,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":650,"statistic":22},[],{"title":648},{"VI":649},"Department of Geography and Natural Hazards, Research Institute of Shakhes Pajouh, Isfahan, Iran",[],{"title":652,"gsAuthor":654},{"VI":653},"Mohammad Reza Mansouri Daneshvar",{"VOID":655},"[\"eu_7ldoAAAAJ\"]",{"id":657,"sortIndex":199,"researcher":22,"roles":658,"affiliations":659,"properties":668,"displayName":670,"givenName":22,"familyName":22},"78253fd5-6eb8-4706-92fc-f74663cd1a6c",[346],[660],{"id":661,"sortIndex":23,"affiliation":662,"properties":22},"185f0742-c232-469f-b01f-c23eadede5b2",{"id":661,"createTime":22,"updateTime":22,"relativeEntities":663,"slug":22,"properties":664,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":667,"statistic":22},[],{"title":665},{"VI":666},"Department of Management, University of Turin, Torino, Italy",[],{"title":669,"gsAuthor":671},{"VI":670},"Fahimeh Khatami",{"VOID":672},"[\"XK-GOhYAAAAJ\"]",{"id":674,"sortIndex":374,"researcher":22,"roles":675,"affiliations":676,"properties":685,"displayName":687,"givenName":22,"familyName":22},"00a08193-e458-44fa-a75d-3396c2e683b9",[346],[677],{"id":678,"sortIndex":23,"affiliation":679,"properties":22},"251d5119-2da0-4a68-8715-452e79262fc9",{"id":678,"createTime":22,"updateTime":22,"relativeEntities":680,"slug":22,"properties":681,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":684,"statistic":22},[],{"title":682},{"VI":683},"Department of Urban Planning and Design, Mashhad Branch, Islamic Azad University, Mashhad, Iran",[],{"title":686},{"VI":687},"Farzin Zahed",{"url":637,"publisher":689,"properties":742},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":690,"slug":10,"properties":691,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":695,"manageAffiliations":711,"indexDatabases":722,"url":22,"thumbnailPath":22,"statistic":737,"gsStatistic":22,"type":150,"analyzePriority":22},[],{"issn":692,"title":693,"eissn":694},{"VOID":15},{"EN":17},{"VOID":13},[696,699,703,707],{"id":26,"createTime":22,"updateTime":22,"relativeEntities":697,"label":698,"description":22,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":29},{"id":31,"createTime":22,"updateTime":22,"relativeEntities":700,"label":701,"description":702,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":34},{},{"id":37,"createTime":22,"updateTime":22,"relativeEntities":704,"label":705,"description":706,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":40},{},{"id":43,"createTime":22,"updateTime":22,"relativeEntities":708,"label":709,"description":710,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":46},{},[712,717],{"id":50,"createTime":22,"updateTime":22,"relativeEntities":713,"slug":22,"properties":714,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":716,"statistic":22},[],{"title":715},{"EN":54},[56],{"id":58,"createTime":22,"updateTime":22,"relativeEntities":718,"slug":22,"properties":719,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":721,"statistic":22},[],{"title":720},{"EN":62},[56],[723,730],{"id":66,"indexDatabase":724,"url":79,"indexYears":22,"academicFieldIds":729,"indexDatabaseRanking":22},{"id":68,"createTime":22,"updateTime":22,"relativeEntities":725,"label":726,"description":727,"key":75,"publicationTags":728,"standard":22},[],{"EN":71,"VI":71},{"EN":73,"VI":74},[77,78],[81],{"id":83,"indexDatabase":731,"url":94,"indexYears":95,"academicFieldIds":736,"indexDatabaseRanking":101},{"id":85,"createTime":22,"updateTime":22,"relativeEntities":732,"label":733,"description":734,"key":91,"publicationTags":735,"standard":22},[],{"EN":88,"VI":88},{"EN":88,"VI":90},[93],[97,98,99,100],{"impactFactor":23,"impactFactorByYear":738,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":739,"totalCitation":126,"totalCitationByYear":740,"totalCitationPerPublication":138,"totalCitationPerPublicationByYear":741,"hindexLast5Year":149,"hindex":149},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":120,"2020":121,"2021":122,"2022":123,"2023":124,"2024":125},{"2015":128,"2016":129,"2017":130,"2018":131,"2019":132,"2020":133,"2021":134,"2022":135,"2023":136,"2024":137},{"2015":140,"2016":141,"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147,"2023":148,"2024":104},{"pages":743,"volume":745},{"VOID":744},"1161-1170",{"VOID":746},"3",43,{"total":747,"publishYear":749,"statisticByYear":750},2017,{"2018":374,"2019":751,"2020":752,"2021":753,"2022":544,"2023":753,"2024":751,"2025":544,"2026":544},6,10,5,"2017-08-14","2026-07-26T06:09:53.043+00:00",[101,77],{"id":758,"createTime":759,"updateTime":760,"relativeEntities":761,"slug":762,"properties":763,"entityType":172,"verifyStatus":173,"verifyTime":774,"verifyNote":175,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":775,"fullTextUrl":22,"authors":776,"publicationType":215,"publisherRelationship":809,"citationCount":22,"citationInfo":22,"publishDate":868,"publishYear":869,"citationAnalyzeStatus":21,"lastCitationAnalyze":760,"indexDatabases":870,"openAccess":22,"references":22,"isForceReanalyzing":322},"984c556d-5c4d-4b39-8165-679c3ba91f2d","2024-01-18T06:51:06.823+00:00","2026-07-23T17:50:15.230+00:00",[],"Appraisal-of-groundwater-quality-in-upper-Manimuktha-sub-basin-Vellar-river-Tamil-Nadu-India-by-using-Water-Quality-Index-WQI-and-multivariate-statistical-techniques",{"abstract":764,"title":766,"gsPaper":768,"references":770,"doi":772},{"EN":765},"Groundwater is a major natural resource for drinking and irrigation purpose. The overexploited of groundwater is increase year by year and quality of groundwater simultaneously decreases. Groundwater quality is the main issue because water is linked with our metabolism. In order to know the groundwater pollution and controlling factors of groundwater quality in the upper Manimuktha sub basin, Vellar river, Tamil Nadu, India. Forty eight groundwater samples were collected from entire study area on January 2014 and analysed for physicochemical properties. Major ions were as abundance of Na > Ca > Mg > K, and HCO3 > Cl > SO4 > NO3 respectively. Multivariate statistical analyses display the good correlation between all the physicochemical parameters except pH and F. The dendrogram reveals cluster 3 (EC and TDS), cluster 2 (alkalinity, TH, HCO3) and cluster 1 (F, K, NO3, Ca, Mg, Na, SO4, Cl). The hydrochemical processes reveal rock-weathering interactions and ion-exchange processes play an important role in groundwater quality of the study area. The WQI indicates 50.03% of the samples fall in excellent to good for drinking in the center of the study area. Remaining samples fall poor to very poor categories, signifying northern and southern side mainly polluted. Maximum of lakes located in the northern side also indicate poor quality, because of the contamination of wastewater at or near the lakes, migrate in the groundwater. This study has shown the great combination of GIS, statistical analysis and WQI in assessing groundwater quality give a clear view for decision makers can plan better for the operation and maintenance of groundwater resources.",{"EN":767},"Appraisal of groundwater quality in upper Manimuktha sub basin, Vellar river, Tamil Nadu, India by using Water Quality Index (WQI) and multivariate statistical techniques",{"VOID":769},"[\"7473522730342933421\"]",{"VOID":771},"Alobaidy AHMJ., Abid HS, Maulood BK (2010) Application of water quality index for assessment of Dokan Lake ecosystem, Kurdistan Region, Iraq. J Water Resour Prot 2:792–798\nAmadi AN (2011) Assessing the effects of Aladimma dumpsite on soil and groundwater using water quality index and factor analysis. Aust J Basic Appl Sci 5(11):763–770\nAmerican Public Health Association (APHA) (1998) Standard methods for the examination of water and wastewater, 20th. American Public Health Association, American Water Works Association, Water Environment Federation, Washington, DC\nArulbalaji P, Gurugnanam B (2016) Groundwater quality assessment using geospatial and statistical tools in Salem District, Tamil Nadu, India. Appl Water Sci. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13201-016-0501-5\nBIS (1991) Bureau of Indian Standards—Indian standard specification for drinking water, IS, p 10500\nBoateng TK, Opoku F, Acquaah SO (2016) Groundwater quality assessment using statistical approach and water quality index in Ejisu-Juaben Municipality, Ghana. Environ Earth Sci. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-015-5105-0\nBurrough PA, Mc Donnell RA (1998) Principles of geographical information systems for land resources assessment. Oxford University Press, New York, pp 1–333\nCerling TE, Pederson BL, Damm KLV (1989) Sodium calcium ion exchange in the weathering of shales: implication from global weathering budgets. Geology 17:552–554\nChan HJ (2001) Effect of land use and urbanization on hydrochemistry and contamination of groundwater from Taejon area, Korea. J Hydrol 253:194–210\nDeepa S, Venkateswaran S, Ayyandurai R, Kannan R, Vijay Prabhu M (2016) Groundwater recharge potential zones mapping in upper Manimuktha Sub basin Vellar river Tamil Nadu India using GIS and remote sensing techniques. Model Earth Syst Environ. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40808-016-0192-9\nEdmunds WM, Smedley PL (2000) Residence time indicators in groundwater: The East Midlands Triassic sandstone aquifer. Appl Geochem 15:737–752\nEhteshami M, Salari M, Zaresefat M (2016) Sustainable development analyses to evaluate groundwater quality and quantity management Model Earth Syst Environ. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40808-016-0196-5\nElkrail AB, Obied BA (2013) Hydrochemical characterization and groundwater quality in Delta Tokar alluvial plain, Red Sea coast-Sudan. Arab J Geosci. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-012-0594-6\nElliot T, Andrews JN, Edmunds WM (1999) Hydrochemical trends, paleorecharge and groundwater ages in the fissured Chalk aquifer of the London and Berkshire basins UK. Appl Geochem 14:333–363\nFantong WY, Satake H, Aka FT, Ayonghe SN, Asai K, Mandal AK (2009) Hydrochemical and isotopic evidence of recharge, apparent age, and flow direction of groundwater in Mayo Tsanaga River Basin, Cameroon: bearings on contamination. Environ Earth Sci. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-009-0173-7\nFisher RS, Mullican WF (1997) Hydrochemical evolution of sodium sulphate and sodium chloride groundwater beneath the Northern Chihuahuan desert, Trans-Pecos, Rexas, USA. Hydrogeol J 10:455–474\nFreeze RA, Cherry JA (1979) Groundwater. Prentice-Hall, New Jersey, p 604\nGanyaglo SY, Banoeng-Yakubo B, Osae S, Dampare SB, Fianko JR (2011) Water quality assessment of groundwater in some rock types in parts of the eastern region of Ghana. Environ Earth Sci 62(5):1055–1069\nGibrilla A, Bam EKP, Adomako D (2011) Application of water quality index (WQI) and multivariate analysis for groundwater quality assessment of the Birimian and Cape Coast granitoid complex: Densu river basin of Ghana. Water Qual Expo Health. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12403-011-0044-9\nGnanachandrasamy G, Ramkumar T, Venkatramann S, Anithamary I, Vasudevan S (2012) GIS Based hydrogeochemical characteristics of groundwater quality in Nagapattinam District, Tamilnadu, India. Carpath. J Earth Environ Sci 7(3):205–210\nGnanachandrasamy G, Ramkumar T, Venkatramanan S (2015) Accessing groundwater quality in lower part of Nagapattinam district, Southern India: using hydrogeochemistry and GIS interpolation techniques. Appl Water Sci. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13201-014-0172-z\nGopinath S, Srinivasamoorthy K, Vasanthavigar M, Saravanan K, Prakash R, Suma CS, Senthilnathan D (2016) Hydrochemical characteristics and salinity of groundwater in parts of Nagapattinam district of Tamil Nadu and the Union Territory of Puducherry, India. 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Landsat-OLI imagery, topographical map and geological map of Nigeria were used for this study. The methodology involves subjecting the imagery to preprocessing algorithms, linear\u002Fedge enhancement, directional filtering, contrast stretching using ENVI 4.7 software. The lineaments were extracted automatically using PCI Geomatica software while ArcGIS and Rockworks software were used to plot the lineament density map and lineament trend respectively. The result shows the total number and length of lineaments to be 197 and 278.85 km respectively. Shorter lineaments constitute about 40% of the total number of lineaments. The lineaments density varies from 0 to 2.40 km\u002Fsq.km, and areas with 1.32–2.40 km\u002Fsq.km and 0.82 km\u002Fsq.km reflect high and moderate degree of rock fracturing which makes these areas suitable target for groundwater exploitation as they possess more lineaments. The lineament trend results showed two prominent trends E–W, N–S and ENE–WSW. Other minor trends are also observed in the study area. These trends validate with results of earlier local studies and with directions of prominent geological structures and features of the study area. The discovery and delineation of the geological structures (lineaments) will serve as direct guides for decision makers to accurately site productive boreholes that will solve the problem of water scarcity in the study area, thereby promoting good developmental policy.",{"EN":881},"Automated Geological lineaments mapping for groundwater exploration in the basement complex terrain of Akoko-Edo area, Edo-State Nigeria using remote sensing techniques",{"VOID":883},"[\"17233576832636284524\"]",{"VOID":885},"Abdullahi BU, Rai JK, Momoh M, Udensi EE (2013) Effect of lineaments on groundwater occurrence. 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Folds and fractures, vol 2, xi + 391. Academic Press, Cambridge\nRao NS, Chakradhar GKJ, Srinivas V (2001) Identification of groundwater potential zones using remote sensing techniques in and around Guntur Town, Andhra Pradesh, India. J Indian Soc Remote Sens 29:69\nReddy GPOBI, Mouli KC, Srivastav SK, Srinivas CV, Maji AK (2000) Evaluation of ground water potential zones using remote sensing data—a case study of Gaimukh Watershed, Bhandard District, Maharashtra. J Indian Soc Remote Sens 28:19–32\nSabins FF (1996) Remote sensing: principles and interpretation, 3rd edn. W. H. Freeman and Company, New York, p 494\nSankar K (2002) Evaluation of groundwater potential zones using remote sensing data In Upper Vaigai River Basin, Tamil Nadu, India. J Indian Soc Remote Sens 30(3):119–129\nStefouli M, Angellopoulos A, Perantonis S, Vassilas N, Ambazis N, Charou E (1996) Integrated analysis and use of remotely sensed data for the seismic risk assessment of the southwest Peloponessus Greece. In: First Congress of the Balkan Geophysical Society, 23–27 September, Athens Greece\nYassaghi A (2006) Integration of Landsat imagery interpretation and geomagnetic data on verification of deep-seated transverse fault lineaments in SE Zagrosa, Iran. Int J Remote Sens 27:4529–4544",{"VOID":887},"10.1007\u002Fs40808-018-0511-4","2024-05-14T23:43:00.109+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40808-018-0511-4",[891,908],{"id":892,"sortIndex":23,"researcher":22,"roles":893,"affiliations":894,"properties":903,"displayName":905,"givenName":22,"familyName":22},"c1426832-a655-459e-9dcc-3c4eb4ffd5fb",[346],[895],{"id":896,"sortIndex":23,"affiliation":897,"properties":22},"61d83ef5-ff31-4def-95e0-2eba9047f195",{"id":896,"createTime":22,"updateTime":22,"relativeEntities":898,"slug":22,"properties":899,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":902,"statistic":22},[],{"title":900},{"VI":901},"Department of Geology, University of Nigeria, Nsukka, Nigeria",[],{"title":904,"gsAuthor":906},{"VI":905},"Olaniran E. Aluko",{"VOID":907},"[\"Ev_LZp8AAAAJ\"]",{"id":909,"sortIndex":199,"researcher":22,"roles":910,"affiliations":911,"properties":918,"displayName":920,"givenName":22,"familyName":22},"e86405d6-03ee-48e7-8593-7c63db3ce4d4",[346],[912],{"id":896,"sortIndex":23,"affiliation":913,"properties":22},{"id":896,"createTime":22,"updateTime":22,"relativeEntities":914,"slug":22,"properties":915,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":917,"statistic":22},[],{"title":916},{"VI":901},[],{"title":919,"gsAuthor":921},{"VI":920},"Ogbonnaya Igwe",{"VOID":922},"[\"A6f1rq8AAAAJ\"]",{"url":889,"publisher":924,"properties":977},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":925,"slug":10,"properties":926,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":930,"manageAffiliations":946,"indexDatabases":957,"url":22,"thumbnailPath":22,"statistic":972,"gsStatistic":22,"type":150,"analyzePriority":22},[],{"issn":927,"title":928,"eissn":929},{"VOID":15},{"EN":17},{"VOID":13},[931,934,938,942],{"id":26,"createTime":22,"updateTime":22,"relativeEntities":932,"label":933,"description":22,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":29},{"id":31,"createTime":22,"updateTime":22,"relativeEntities":935,"label":936,"description":937,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":34},{},{"id":37,"createTime":22,"updateTime":22,"relativeEntities":939,"label":940,"description":941,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":40},{},{"id":43,"createTime":22,"updateTime":22,"relativeEntities":943,"label":944,"description":945,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":46},{},[947,952],{"id":50,"createTime":22,"updateTime":22,"relativeEntities":948,"slug":22,"properties":949,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":951,"statistic":22},[],{"title":950},{"EN":54},[56],{"id":58,"createTime":22,"updateTime":22,"relativeEntities":953,"slug":22,"properties":954,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":956,"statistic":22},[],{"title":955},{"EN":62},[56],[958,965],{"id":66,"indexDatabase":959,"url":79,"indexYears":22,"academicFieldIds":964,"indexDatabaseRanking":22},{"id":68,"createTime":22,"updateTime":22,"relativeEntities":960,"label":961,"description":962,"key":75,"publicationTags":963,"standard":22},[],{"EN":71,"VI":71},{"EN":73,"VI":74},[77,78],[81],{"id":83,"indexDatabase":966,"url":94,"indexYears":95,"academicFieldIds":971,"indexDatabaseRanking":101},{"id":85,"createTime":22,"updateTime":22,"relativeEntities":967,"label":968,"description":969,"key":91,"publicationTags":970,"standard":22},[],{"EN":88,"VI":88},{"EN":88,"VI":90},[93],[97,98,99,100],{"impactFactor":23,"impactFactorByYear":973,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":974,"totalCitation":126,"totalCitationByYear":975,"totalCitationPerPublication":138,"totalCitationPerPublicationByYear":976,"hindexLast5Year":149,"hindex":149},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":120,"2020":121,"2021":122,"2022":123,"2023":124,"2024":125},{"2015":128,"2016":129,"2017":130,"2018":131,"2019":132,"2020":133,"2021":134,"2022":135,"2023":136,"2024":137},{"2015":140,"2016":141,"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147,"2023":148,"2024":104},{"pages":978,"volume":980},{"VOID":979},"1527-1536",{"VOID":867},{"total":23,"publishYear":869,"statisticByYear":982},{},"2018-09-08","2026-07-21T19:00:02.929+00:00",[101,77],{"id":987,"createTime":988,"updateTime":989,"relativeEntities":990,"slug":991,"properties":992,"entityType":172,"verifyStatus":173,"verifyTime":1003,"verifyNote":175,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1004,"fullTextUrl":22,"authors":1005,"publicationType":215,"publisherRelationship":1053,"citationCount":23,"citationInfo":1112,"publishDate":1115,"publishYear":1113,"citationAnalyzeStatus":617,"lastCitationAnalyze":1116,"indexDatabases":1117,"openAccess":22,"references":22,"isForceReanalyzing":322},"74dcbea7-962c-475a-80c2-b6d83fe8b139","2024-02-17T22:24:25.037+00:00","2026-07-21T08:05:53.319+00:00",[],"Climate-change-vulnerability-assessment-using-a-GIS-modelling-approach-in-ASAL-ecosystem-a-case-study-of-Upper-Ewaso-Nyiro-basin-Kenya",{"abstract":993,"title":995,"gsPaper":997,"references":999,"doi":1001},{"EN":994},"Investments in climate change adaptation for communities and water resources are increasingly benefiting from vulnerability mapping. Such mapping can reveal hotspot areas from climate-related threats and can provide useful information needed for adaptation and resilience building efforts. The arid and semi-arid lands of Kenya are volatile to climate change and pastoralists in this region are highly dependent on climate-sensitive natural resources to sustain their livelihoods, hence any change in climate affects them negatively. This study sought to assess climate vulnerability in upper Ewaso Nyiro basin area using the IPCC approach which defines vulnerability in three components: exposure, sensitivity and adaptive capacity. Twelve indicators were selected based on its proximity to the component being measured and normalization of the indicators was done to ensure consistency of scale ranging from 0 to 100, illustrating a linear relationship to vulnerability 100 being most vulnerable. The study relied on averaging approach of indicators to derive component and vulnerability scores through the process of spatial integration. Results observed high vulnerability in areas exhibiting similar trend in high exposure and lack of adaptive capacity as well as high sensitivity. The highest vulnerability was observed in Korr\u002FNgurunit ward in Marsabit County where approximately 45.45% of the population was noted to be highly vulnerable to climate change impacts, while 54.55% of the population was noted to be moderately vulnerable. This study recommends county government and national government to build targeted resilience efforts within the highly vulnerable communities since this would improve community livelihoods.",{"EN":996},"Climate change vulnerability assessment using a GIS modelling approach in ASAL ecosystem: a case study of Upper Ewaso Nyiro basin, Kenya",{"VOID":998},"[\"16939655220130187613\"]",{"VOID":1000},"Abson DJ, Dougill AJ, Lindsay C (2012) Spatial mapping of socio-ecological vulnerability to environmental change in Southern Africa, vol 32. Sustainability Research Institute, School of Earth and Environment, The University of Leeds, Leeds, pp 1–33\nBalk D, Yetman G (2004) The global distribution of population: evaluating the gains in resolution refinement. Center for International Earth Science Information Network (CIESIN), Columbia University, New York\nBesada H, Sewankambo N, Lisk F, Sage I, Kabasa JD, Willms DG, Dybenko E (2009) Climate change in Africa: adaptation, mitigation and governance challenges. CIGI Special report. https:\u002F\u002Fwww.africaportal.org\u002Fpublications\u002Fclimate-change-in-africa-adaptation-mitigation-and-governance-challenges\u002F. Retrieved 8 May 2019\nChoularton R, Frankenberger T, Kurtz J, Nelson S (2015) Measuring shocks and stressors as part of resilience measurement. Resilience measurement technical working group. Obtenido de technical series no. 5\nde Sherbinin A, Chai-Onn T, Jaiteh M, Mara V, Pistolesi L, Schnarr E, Trzaska S (2015) Data integration for climate vulnerability mapping in West Africa. ISPRS Int J Geo-Inf 4(4):2561–2582\nGlew L, Hudson MD, Osborne PE (2010) Evaluating the effectiveness of community-based conservation in northern Kenya: a report to The Nature Conservancy. Centre for Environmental Sciences, University of Southampton, Southampton\nHall JM, Van Holt T, Daniels AE, Balthazar V, Lambin EF (2012) Trade-offs between tree cover, carbon storage and floristic biodiversity in reforesting landscapes. Landsc Ecol 27(8):1135–1147\nKogan F, Stark R, Gitelson AA, Jargalsaikhan L, Dugrajav C, Tsooj S (2004) Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. Int J Remote Sens 25(14):2889–2896. https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431160410001697619\nMccollum DW et al (2015) Climate change effects on rangelands and rangeland management : affirming the need for monitoring, pp 1–13. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fehs2.1264\nNicol A, Kaur N (2016) Adapting to climate change in the water sector. Overseas Development Institute (ODI). https:\u002F\u002Fwww.odi.org\u002Fsites\u002Fodi.org.uk\u002Ffiles\u002Fodi-assets\u002Fpublications-opinion-files\u002F4118.pdf. Retrieved 19 Aug 2019\nNjoka JT, Yanda P, Maganga F, Liwenga E, Kateka A, Henku A, Schubert C (2016) Kenya: country situation assessment. PRISE working paper. http:\u002F\u002Fprise.odi.org\u002Fwp-content\u002Fuploads\u002F2016\u002F01\u002FLow-Res_Kenya-CSA.pdf. Retrieved 7 Feb 2019\nOjwang GO, Agatsiva J, Situma C (2010) Analysis of climate change and variability risks in the smallholder sector. Environment and Natural Resources. Working Paper (FAO)\nPolley HW, Briske DD, Morgan JA, Wolter K, Bailey D, Brown JR (2013) Climate change and North American rangelands: trends, projections, and implications. Rangeland Ecol Manag 66:493–511",{"VOID":1002},"10.1007\u002Fs40808-019-00695-8","2024-05-12T14:50:07.766+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40808-019-00695-8",[1006,1023,1038],{"id":1007,"sortIndex":23,"researcher":22,"roles":1008,"affiliations":1009,"properties":1018,"displayName":1020,"givenName":22,"familyName":22},"c1ec9598-c7db-47e6-b461-37f882737e8f",[346],[1010],{"id":1011,"sortIndex":23,"affiliation":1012,"properties":22},"268fdea8-2ee4-4e8c-85ab-52b807fa899b",{"id":1011,"createTime":22,"updateTime":22,"relativeEntities":1013,"slug":22,"properties":1014,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1017,"statistic":22},[],{"title":1015},{"VI":1016},"Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, Nyeri, Kenya",[],{"title":1019,"gsAuthor":1021},{"VI":1020},"Grace 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study aimed to investigate the temporal relationship between Land Surface Temperature (LST) and multiple parameters, develop, and evaluate LST prediction models for Tiruchirappalli, India. Using a dataset spanning twenty years of LST data from Tiruchirappalli, LST prediction models were developed incorporating various parameters. Correlation analyses were conducted to assess the suitability and direct impact of multiple parameters, including Aerosol Optical Depth (AOD), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Road Density (RD), elevation, and geographical coordinates on LST during the summer and winter seasons. Model performance was evaluated using the 21st year data. Among the various models employed, the Long Short-Term Memory (LSTM) model demonstrated the highest accuracy in predicting LST values. The LSTM model's performance was further validated, and its ability to predict LST values for the year 2021 was confirmed. The findings indicate that EVI had a significant negative association with LST, while the correlation between AOD and LST varied, being positive in summer and negative in winter. NDWI exhibited a negative correlation, while RD showed a positive correlation with LST. Contrary to expectations, LST values increased with elevation, suggesting the influence of regional geometric and surface characteristics. The LSTM model's high accuracy in predicting LST values makes it a valuable tool for urban planners and decision-makers in mitigating the effects of the Surface Urban Heat Island (SUHI) phenomenon and promoting sustainable urban development. The study's comprehensive analysis sheds light on the intricate interactions between LST and various environmental factors, providing insights that can inform future research and the development of cause-and-effect predictive models for other cities. The findings contribute to our understanding of the SUHI effect and offer practical implications for addressing the challenges it poses in urban environments.",{"EN":1128},"Predicting land surface temperature using data-driven approaches for urban heat island studies: a comparative analysis of correlation with environmental parameters",{"VOID":1130},"[\"5079193393047376494\"]",{"VOID":1132},"Bala R, Prasad R, Pratap Yadav V (2020) A comparative analysis of day and night land surface temperature in two semi-arid cities using satellite images sampled in different seasons. Adv Space Res 66(2):412–425. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.asr.2020.04.009\nBhargava A, Lakmini S, Bhargava S (2017) Urban heat Island effect: it’s relevance in urban planning. J Biodivers Endang Sp 5(187):2020\nBreiman L (2001) Random forests. Mach Learn 45(1):5–32. https:\u002F\u002Fdoi.org\u002F10.1023\u002FA:1010933404324\nCensus of India, 1991. URL: https:\u002F\u002Flsi.gov.in:8081\u002Fjspui\u002Fbitstream\u002F123456789\u002F6744\u002F1\u002F35981_1991_TIR.pdf. Last accessed: 28\u002F07\u002F2022\nChakraborty T, Sarangi C, Tripathi SN (2017) Understanding diurnality and inter-seasonality of a sub-tropical urban heat island. Bound-Layer Meteorol 163:287–309. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10546-016-0223-0\nChen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794).\nCrutzen PJ (2004) New directions: the growing urban heat and pollution “island” effect - impact on chemistry and climate. Atmos Environ 38(21):3539–3540\nEquere V, Mirzaei PA, Riffat S (2020) Definition of a new morphological parameter to improve prediction of urban heat island. Sustain Cities Soc 56:102021. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scs.2020.102021\nGuha S, Govil H, Taloor AK, Gill N, Anindita Dey A (2022) Land surface temperature and spectral indices: a seasonal study of Raipur City. Geodesy Geodyn 13:72–82\nGunawardena KR, Wells MJ, Kershaw T (2017) Utilising green and bluespace to mitigate urban heat island intensity. Sci Total Environ 584:1040–1055\nHaizhu Z, Neng Z, Qingqin W (2021) Modelling and simulation of the urban heat island effect in a tropical seaside city considering multiple street canyons. Indoor Built Environ 30(8):1124–1141\nHarishkumar KS, Yogesh KM, Gad I (2020) Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models. Proc Comput Sci 171:2057–2066\nHasnahena D, Sarker SC, Islam MS, Rahman MZ, Islam MN (2023) Modeling on microclimatic variation of land surface temperature and vegetation cover at Rangpur City in Bangladesh. Model Earth Syst Environ 9(1):1009–1028\nHastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York, pp 1–758\nHibbard K, Hoffman F, Huntzinger DN, West T (2017) Changes in land cover and terrestrial biogeochemistry. In: Climate science special report: fourth national climate assessment, vol I. U.S. Global Change Research Program, Washington, pp 277–302. https:\u002F\u002Fdoi.org\u002F10.7930\u002FJ0416V6X\nHochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780\nHou S, Su H, Yao C, Wang ZH (2023) Spatiotemporal patterns of the impact of surface roughness and morphology on urban heat island. Sustain Cities Soc 92:104513\nKafy AA, Abdullah Al F, Rahman MS, Islam M, Al Rakib A, Islam MA, Sattar GS (2021) Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustain Cities Soc 64:102542\nLearn About Heat Islands | US EPA. URL: https:\u002F\u002Fwww.epa.gov\u002Fheatislands\u002Fheat-island-impacts. Last accessed: 28\u002F07\u002F2022\nLeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444\nLevermore G, Cheung H (2012) A low-order canyon model to estimate the influence of canyon shape on the maximum urban heat island effect. Build Serv Eng Res Technol 33(4):371–385\nLi XX, Norford LK (2016) Evaluation of cool roof and vegetations in mitigating urban heat island in a tropical city, Singapore. Urban Clim 16:59–74\nLi L, Tan Y, Ying S, Yu Z, Li Z, Lan H (2014) Impact of land cover and population density on land surface temperature: case study in Wuhan, China. J Appl Remote Sens 8(1):084993\nLi H, Meier F, Lee X, Chakraborty T, Liu J, Schaap M, Sodoudi S (2018) Interaction between urban heat island and urban pollution island during summer in Berlin. Sci Total Environ 636:818–828\nLi B, Liang S, Liu X, Ma H, Chen Y, Liang T, He T (2021) Estimation of all-sky 1 km land surface temperature over the conterminous United States. J Remote Sens Environ 266:112707\nMackey TK, Liang BA (2012) Threats from emerging and re-emerging neglected tropical diseases (NTDs). Infect Ecol Epidemiol 2(1):18667\nMinistry of Coal, Government of India. URL: https:\u002F\u002Fcoal.nic.in\u002Fen\u002Fmajor-statistics\u002Fgeneration-of-thermal-power-from-raw-coal. Last accessed: 28\u002F07\u002F2022\nMohammad P, Goswami A, Chauhan S, Nayak S (2022) Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India. 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A digital terrain model, a.k.a bare-earth model, excludes the bias due to the heights from urban and forests will enable the quantitative terrain characterization. Digital bare-earth models exemplify themselves as an essential input in numerous environmental analyses, especially in floodplain mapping. A forest and buildings removed Copernicus digital elevation model, referred to as FABDEM, is a first, global, open-access, machine learning-based, and 30 m spatial resolution data that simulates a bare-earth model. This article presents a novel approach to validate a digital bare-earth model by considering FABDEM as a case. Highly accurate elevations from the ground reflected photons from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) were used as a reference to check the agreement of FABDEM’s tendency as a bare-earth model. Visual analytics done using the elevation profiles and error metrics confirms the FABDEM’s successful reduction of bias due to urban and forests to a certain extent from its source DEM. However, the error metric shows a positive offset of ~ 3 m while validating the FABDEM’s building removal algorithm, indicating a scope to decrease the building heights to achieve its anticipated objective of generating a bare-earth model in urban areas. In the case of the forest removal algorithm of FABDEM, it has successfully reduced the canopy heights to approximately 50% of its source. Still, the error metrics show a mean absolute error of ~ 14 m, ~ 10 m, and ~ 3 m in the test sites that fall in mountainous areas, rolling hills, and flat regions, respectively, that host different tree types and canopy structures. Our research has also investigated the possible sources of uncertainties and performance factors of FABDEM; these include the predictor variables, the number of regions of training sets, and errors that can accumulate from its original elevation source, i.e., Copernicus GLO-30 DEM.",{"EN":1258},"Accuracy assessment of digital bare-earth model using ICESat-2 photons: analysis of the FABDEM",{"VOID":1260},"[\"13973853665336950672\"]",{"VOID":1262},"AIRBUS (2020) Copernicus DEM: Copernicus digital elevation model product hand book Report AO\u002F1–9422\u002F18\u002FI-LG, European Space Agency. https:\u002F\u002Fspacedata.copernicus.eu\u002Fdocuments\u002F20126\u002F0\u002FGEO1988-CopernicusDEM-SPE-002_ProductHandbook_I1.00.pdf. Accessed 20 September 2022\nAl-Fares W (2013) Historical land use\u002Fland cover classification using remote sensing: a case study of the Euphrates river basin in Syria. 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Photogramm Eng Remote Sens 87:821–830. https:\u002F\u002Fdoi.org\u002F10.14358\u002FPERS.21-00009R2\nLi R, Li H, Hao T, Qiao G, Cui H, He Y, Hai G, Xie H, Cheng Y, Li B (2021b) Assessment of ICESat-2 ice surface elevations over the Chinese Antarctic Research Expedition (CHINARE) route, East Antarctica, based on coordinated multi-sensor observations. Cryosphere 15:3083–3099. https:\u002F\u002Fdoi.org\u002F10.5194\u002Ftc-15-3083-2021\nLi H, Zhao J, Yan B, Yue L, Wang L (2022) Global DEMs vary from one to another: an evaluation of newly released Copernicus, NASA and AW3D30 DEM on selected terrains of China using ICESat-2 altimetry data. Int J Digit Earth 15:1149–1168. https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2022.2094002\nLiu Z, Zhu J, Fu H, Zhou C, Zuo T (2020) Evaluation of the vertical accuracy of open global dems over steep terrain regions using icesat data: a case study over Hunan province. 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