Đánh giá độ tin cậy của hệ thống chuyên gia mờ và máy học cực đoan cho phân vùng độ nhạy lở đất dựa trên hệ thống thông tin địa lý: Nghiên cứu trường hợp từ dãy Himalaya Ấn Độ

Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75:12665 Aghdam IN, Pradhan B, Panahi M (2017) Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environ Earth Sci 76:1–22 Ahmed R, Sajjad H (2018) Analyzing factors of groundwater potential and its relation with population in the Lower Barpani Watershed, Assam, India. Nat Resour Res 27:503–515 Akgun A, Sezer EA, Nefeslioglu HA et al (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34 An K, Kim S, Chae T, Park D (2018) Developing an accessible landslide susceptibility model using open-source resources. Sustainability 10:1–13 Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277 Anbalagan R (1996) Landslide hazard and risk assessment mapping of mountainous terrains—a case study from Kumaun Himalaya, India. Eng Geol 43:237–246 Anbalagan R, Singh B, Chakraborty D, Kohli A (2007) A field manual for landslide investigations. Department of Science Technology Government of India, New Delhi Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (ganga) valley, himalayas. Int J Remote Sens 25:559–572 Barnard PL, Owen LA, Sharma MC, Finkela RC (2001) Natural and human-induced landsliding in the Garhwal Himalaya of Northern India. Geomorphology 40:21–35 Cao Y, Yin K, Alexander DE, Zhou C (2016) Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13:725–736 Cascini L, Critelli S, Gulla G, Di Nocera S (1991) A methodological approach to landslide hazard assessment: a case history. In: Proceedings of the 16th international landslide conference, Balkema, Rotterdam. pp 899–904 Chimidi G, Raghuvanshi TK, Suryabhagavan KV (2017) Landslide hazard evaluation and zonation in and around Gimbi town, western Ethiopia—a GIS-based statistical approach. Appl Geom 9:219–236 Choi J, Oh HJ, Lee HJ et al (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23 Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: The Baganza valley case study (Italian Northern Apennines). Environ Geol 50:941–961 D’Ambrosio D, Di Gregorio S, Iovine G (2003) Simulating debris flows through a hexagonal cellular automata model: SCIDDICA S3–hex. Nat Hazard Earth Syst Sci 3:545–559 Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391 Den Hartog MH, Babuška R, Deketh HJR et al (1997) Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. Int J Approx Reason 16:43–66 Dhakal AS, Sidle RC (2003) Long-term modelling of landslides for different forest management practices. Earth Surf Process Landf 28:853–868 Erener A, Sebnem H, Düzgün B (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68 Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66:327–343 Freund JE (1992) Mathematical statistics, 5th edn. Printice-Hall of India Pvt. Ltd., New Delhi Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44:147–161 Gokceoglu C, Sonmez H, Nefeslioglu HA et al (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81:65–83 Grima MA, Babuška R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349 Gupta P, Anbalagan R (1997) Slope stability of Tehri Dam Reservoir Area, India, using landslide hazard zonation (LHZ) mapping. Q J Eng Geol Hydrogeol 30:27–36. https://doi.org/10.1144/GSL.QJEGH.1997.030.P1.03 Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment. Himal Eng Geol 28:119–131 Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: Theory and applications. Neurocomputing 70:489–501. https://doi.org/10.1007/s10462-013-9405-z Huang F, Yin K, Huang J et al (2017) Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng Geol 223:11–22 Humbert M (1977) Risk Mapping of areas exposed to Movements of Soil and Sub-soil: French “Zermos” maps. Bull Int Assoc Eng Geol 16:80–82 Iovine G, Di Gregorio S, Lupiano V (2003) Debris-flow susceptibility assessment through cellular automata modeling: an example from 15 to 16 December 1999 disaster at Cervinara and San Martino Valle Caudina (Campania, Southern Italy). Nat Hazard Earth Syst Sci 3:457–468 Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58:271–289 Juang CH, Lee DH, Sheu C (1992) Mapping slope failure potential using fuzzy sets. J Geotech Eng ASCE 118:475–494 Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri Reservoir Rim Region, Uttarakhand. J Geol Soc India 87:271–286 Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility mapping and prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295:115–125 Lee S (2007) Landslide susceptibility mapping using an artificial neural network in the Gangneung are, Korea. Int J Remote Sens 28:4763–4783 Lee S, Min K (2001) Statistical analyses of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113 Lian C, Zeng Z, Yao W, Tang H (2014) Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis. Neural Comput Appl 24:99–107 Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13 Martha TR, Kumar KV (2013) September, 2012 landslide events in Okhimath, India-an assessment of landslide consequences using very high resolution satellite data. Landslides 10:469–479 Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological geomorphological and biological applications. Hydrol Process 5:3–30 Nefeslioglu HA, Gokceoglu C, Sonmez H (2006) Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles. Eng Geol 85:251–269 Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36:325–334 Peethambaran B, Anbalagan R, Shihabudheen KV (2018) Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system—a comparative study. Nat Hazards. https://doi.org/10.1007/s11069-018-3532-4 Pham BT, Tien Bui D, Pourghasemi HR et al (2017) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128:255–273 Pistocchi A, Luzi L, Napolitano P (2002) The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41:765–775 Polykretis C, Ferentinou M, Chalkias C (2014) A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull Eng Geol Environ 74:27–45 Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349 Saha AK, Gupta RP, Sarkar I et al (2005) An approach for GIS-based statistical landslide susceptibility zonation-with a case study in the Himalayas. Landslides 2:61–69 Sahana M, Sajjad H (2017) Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag District, India. J Mt Sci 14:2150–2167 Sahana M, Dutta S, Sajjad H (2018a) Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. Int J Urban Sci 0:1–21 Sahana M, Hong H, Sajjad H (2018b) Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Sci Total Environ 628–629:1557–1566 Sahana M, Hong H, Sajjad H et al (2018c) Assessing deforestation susceptibility to forest ecosystem in Rudraprayag District, India using fragmentation approach and frequency ratio model. Sci Total Environ 627:1264–1275 Sarkar S, Kanungo DP, Sharma S (2015) Landslide hazard assessment in the upper Alaknanda valley of Indian Himalayas. Geomat Nat Hazards Risk 6:308–325 Sati SP, Naithani A, Rawat GS (1998) Landslides in the Garhwal Lesser Himalaya, UP, India. Environmentalist 18:149–155 Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219 Shihabudheen KV, Peethambaran B (2017) Landslide displacement prediction technique using improved neuro-fuzzy system. Arab J Geosci. https://doi.org/10.1007/s12517-017-3278-4 Shihabudheen KV, Pillai GN, Peethambaran B (2017) Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS). Appl Soft Comput 61:892–904 Shou KJ, Wang CF (2003) Analysis of the Chiufengershan landslide triggered by the 1999 Chi–Chi earthquake in Taiwan. Eng Geol 68:237–250 Sonmez H, Gokceoglu C, Ulusay R (2003) An application of fuzzy sets to the Geological Strength Index (GSI) system used in rock engineering. Eng Appl Artif Intell 16:251–269 Tazik E, Jahantab Z, Bakhtiari M et al (2014) Landslide susceptibility mapping by combining the three methods fuzzy logic, frequency ratio and analytical hierarchy process in Dozain basin. XL:15–17. https://doi.org/10.5194/isprsarchives-XL-2-W3-267-2014 Temesgen B, Mohammed MU, Korme T (2001) Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet area, Ethiopia. Phys Chem Earth Part C Solar Terr Planet Sci 26:665–675 Tien Bui D, Tuan TA, Klempe H et al (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378 Valdiya KS (1980) Geology of Kumaun Lesser Himalaya. Wadia Institute Himalayan Geology, Dehradun Varnes DJ (1978) Slope movement types and processes. Transp Res Board Spec Rep 11–33. In: Special report 176: landslides: analysis and control, transportation research board, Washington, DC Vasu NN, Lee SR (2016) A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea. Geomorphology 263:50–70 Wright RH, Nilsen TH (1974) Isopleth map of landslide deposits, Southern San Francisco Bay Region, California. US Geological Survey Miscellaneous Field Studies Map, MF-550 (Scale 1:250,000) Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci 35:1125–1138 Yilmaz C, Topal T, Süzen ML (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak–Turkey). Environ Earth Sci 65:2161–2178 Zhou G, Esaki T, Mitani Y et al (2003) Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Eng Geol 68:373–386 Zhou C, Yin K, Cao Y et al (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the three Gorges Reservoir Area, China. Comput Geosci 112:23–37 Zhu AX, Wang R, Qiao J et al (2014) An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. Geomorphology 214:128–138 Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577