Spatial prediction of flood-prone areas using geographically weighted regression

Environmental Advances - Tập 6 - Trang 100118 - 2021
Jia Min Lin1, Lawal Billa1
1School of Environmental and Geographical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih, Selangor 43500, Malaysia

Tài liệu tham khảo

Al-Juaidi, 2018, Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors, Arab. J. Geosci., 11, 765, 10.1007/s12517-018-4095-0 Alam, 2021, Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh, Quat. Int., 575-576, 295, 10.1016/j.quaint.2020.04.047 Alhija, 2010, Factor analysis: an overview and some contemporary advances, 162 Ali, 2020, GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia, Ecol. Indic., 117, 10.1016/j.ecolind.2020.106620 Arora, 2019, Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models, Geocarto International, 1 Australian Urban Research Infrastructure Network, 2021. Moran's I. [online] Available at: < https://aurin.org.au/resources/workbench-user-guides/portal-user-guides/analysing-your-data/spatial-autocorrelation-tools/morans-i/ >[Accessed 29 August 2021]. Ayalew, 2005, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, Central Japan, Geomorphology, 65, 15, 10.1016/j.geomorph.2004.06.010 Bajabaa, 2014, Flash flood hazard mapping based on quantitative hydrology, geomorphology and GIS techniques (case study of Wadi Al Lith, Saudi Arabia), Arab. J. Geosci., 7, 2469, 10.1007/s12517-013-0941-2 Beven, 1979, A physically based, variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, 43, 10.1080/02626667909491834 Bhat, 2019, Flood hazard assessment of upper Jhelum basin using morphometric parameters, Environ. Earth Sci., 78 Bhatt, 2014, Morphometric analysis to determine floods in the Upper Krishna basin using Cartosat DEM, Geocarto Int., 29, 878, 10.1080/10106049.2013.868042 Blachowski, 2016, Application of GIS spatial regression methods in assessment of land subsidence in complicated mining conditions: case study of the Walbrzych coal mine (SW Poland), Nat. Hazards, 84, 997, 10.1007/s11069-016-2470-2 Braun, 2011, Exploratory regression analysis: a tool for selecting models and determining predictor importance, Behav. Res. Methods, 43, 331, 10.3758/s13428-010-0046-8 Brunsdon, 1996, Geographically weighted regression: a method for exploring spatial nonstationarity, Geogr. Anal., 28, 281, 10.1111/j.1538-4632.1996.tb00936.x Brunsdon, 1999, Some notes on parametric significance tests for geographically weighted regression, J. Reg. Sci., 39, 497, 10.1111/0022-4146.00146 Chalkias, 2014, Landslide susceptibility, Peloponnese peninsula in South Greece, J. Maps, 10, 211, 10.1080/17445647.2014.884022 Chalkias, 2020, Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression, Bull. Eng. Geol. Environ., 79, 2799, 10.1007/s10064-020-01733-x Chang, 2018, Building ANN-based regional multi-step-ahead flood inundation forecast models, Water, 10, 1283, 10.3390/w10091283 Chau, 2005, Comparison of several flood forecasting models in Yangtze River, J. Hydrol. Eng., 10, 485, 10.1061/(ASCE)1084-0699(2005)10:6(485) Chun, 2017, A spatial disaster assessment model of social resilience based on geographically weighted regression, Sustainability, 9, 2222, 10.3390/su9122222 Dano, 2019, Flood susceptibility mapping using GIS-based analytic network process: a case study of Perlis, Malaysia, Water, 11, 615, 10.3390/w11030615 Daoud, 2017, Multicollinearity and regression analysis, J. Phys. Conf. Ser., 949, 10.1088/1742-6596/949/1/012009 Department of Irrigation and Drainage [DID], 2011. Review of the national water resources (2000-2050) and formulation of national water resources policy: final report, volume 11 – Terengganu. [pdf] Available at: < https://www.water.gov.my/jps/resources/PDF/Hydrology%20Publication/Vol11Terengganu.pdf >[Accessed 4 October 2020]. Department of Irrigation and Drainage [DID], 2017. Managing the flood problem in Malaysia. [online] Available at: < https://www.water.gov.my/jps/resources/auto%20download%20images/584130f6ea786.pdf >[Accessed 11 August 2020]. Düzgün, 2008, Spatial and geographically weighted regression, 1073 Erener, 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, 10.1007/s10346-009-0188-x Farhan, 2016, Morphometric analysis and flash floods assessment for drainage basins of the Ras En Naqb area, South Jordan using GIS, J. Geosci. Environ. Prot., 4, 9 Finnish Environmental Institute, 2020. Flood mapping. [online] Available at: < https://www.ymparisto.fi/en-US/Waters/Floods/Flood_risk_management/Flood_risk_management_planning/Flood_mapping/Flood_mapping(8888) > [Accessed 17 September 2020]. Fu, 2014, Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China, Biogeosciences, 11, 2401, 10.5194/bg-11-2401-2014 Ghalkhani, 2012, Application of surrogate artificial intelligent models for real-time flood routing, Water Environ. J., 27, 535, 10.1111/j.1747-6593.2012.00344.x Goodell, 2006, Flood Inundation Mapping using HEC-RAS, Obras y Proyectos, Edicion No2, Primavera 2006., 18 Guisan, 1999, GLM versus CCA spatial modeling of plant species distribution, Plant Ecol., 143, 107, 10.1023/A:1009841519580 Hamzah, 2005, Roadmap Toward Effective Flood Hazard Mapping in Malaysia Hong, 2017, Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China), Geomat. Nat. Hazards Risk, 8, 1997, 10.1080/19475705.2017.1403974 Horton, 1932, Drainage-basin characteristics, Eos, Trans. Am. Geophys. Union, 13, 350, 10.1029/TR013i001p00350 Horton, 1945, Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology, Geol. Soc. Am. Bull., 56, 275, 10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2 Huo, 2012, Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China, Int. J. Environ. Res. Public Health, 9, 995, 10.3390/ijerph9030995 Islam, 2021, Flood susceptibility modelling using advanced ensemble machine learning models, Geosci. Front., 12 Ivić, 2019, Artificial intelligence and geospatial analysis in disaster management, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 161, 10.5194/isprs-archives-XLII-3-W8-161-2019 Iya, 2014, Floods in Malaysia: historical reviews, causes, effects and mitigations approach, Int. J. Interdiscip. Res. Innov., 2, 59 Jenness, J., 2006. Topographic position index (tpi_jen.avx) extension for ArcView 3.x, v. 1.2. [pdf] Jenness Enterprises. Available at: < http://www.jennessent.com/downloads/TPI_Documentation_online.pdf >[Accessed 30 August 2021]. Jothimani, 2020, Flood susceptibility modeling of Megech river catchment, Lake Tana basin, North Western Ethiopia, using morphometric analysis, Earth Syst. Environ., 5, 353, 10.1007/s41748-020-00173-7 Kia, 2012, An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia, Environ. Earth Sci., 67, 251, 10.1007/s12665-011-1504-z Konadu, 2009, Digital elevation models and GIS for watershed modelling and flood prediction – a case study of Accra Ghana, 325 Konrad, C.P., 2003. Effects of urban development on floods. [pdf] Available at: < https://pubs.usgs.gov/fs/fs07603/pdf/fs07603.pdf >[Accessed 22 August 2021]. Kopecký, 2021, Topographic wetness index calculation guidelines based on measured soil moisture and plant species composition, Sci. Total Environ., 757, 10.1016/j.scitotenv.2020.143785 Kurji, 2021, Spatial variability in factors influencing maternal health service use in Jimma Zone, Ethiopia: a geographically-weighted regression analysis, BMC Health Services Research., 21 Kusler, 2016 Li, 2020, Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: a case study of Qingchuan Area, China, Appl. Sci., 10, 1107, 10.3390/app10031107 Littidej, 2019, Built-up growth impacts on digital elevation model and flood risk susceptibility prediction in Muaeng district, Nakhon Ratchasima (Thailand), Water, 11, 1496, 10.3390/w11071496 Mahmood, 2019, Flash flood susceptibility modelling using geomorphometric approach in the Ushairy Basin, eastern Hindu Kush, J. Earth Syst. Sci., 128, 97, 10.1007/s12040-019-1111-z Maier, 2000, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ. Model. Softw., 15, 101, 10.1016/S1364-8152(99)00007-9 MalayMail, 2021. Flood situation in Terengganu worsens, more than 10,000 evacuated in Kemaman. Malay Mail, [online] 7 January. Available at: < https://www.malaymail.com/news/malaysia/2021/01/07/flood-situation-in-terengganu-worsens-more-than-10000-evacuated-in-kemaman/1938368 > [Accessed 9 April 2021]. Mandrekar, 2010, Receiver operating characteristic curve in diagnostic test assessment, J. Thorac. Oncol., 5, 1315, 10.1097/JTO.0b013e3181ec173d Mateo, 2014, A Comparison of Statistical Methods to Standardize Catch-Per-Unit-Effort of the Alaska Longline Sablefish Fishery, 1 Mathur, 2015, Spatial autocorrelation analysis in plant population: an overview, J. Appl. Nat. Sci., 7, 501, 10.31018/jans.v7i1.639 Melton, 1957 Miller, 1953 Mohammadi, 2020, Flood detection and susceptibility mapping using sentinel-1 time series, alternating decision trees, and bag-ADTree models, Complexity, 2020, 1, 10.1155/2020/4271376 Nakaya, T., Charlton, M., Brunsdon, C., Lewis, P., Yao, J. and Fotheringham, A.S., 2016. GWR4.09 user manual: GWR4 windows application for geographically weighted regression modelling. [pdf] Available at: < https://geoplan.asu.edu/sites/default/files/SparcFiles/gwr4manual_409.pdf >[Accessed 13 September 2020]. 2009 Nayar, 2013, Quantitative Morphometric analysis of Kosasthalaiyar sub basin (Chennai basin) using remote sensing (SRTM) data and GIS techniques, Int. J. Geomat. Geosci., 4, 89 Obeidat, 2021, Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies, J. Flood Risk Manag., 14, e12711, 10.1111/jfr3.12711 Othman, 2018, Engagement of local heroes in managing flood disaster: lessons learnt from the 2014 flood of Kemaman, Terengganu, Malaysia Park, 2015, A comparative analysis of landslide susceptibility assessment by using global and spatial regression methods in Inje Area, Korea, J. Korean Soc. Surv. Geod. Photogramm. Cartogr., 33, 579, 10.7848/ksgpc.2015.33.6.579 Pradhan, 2010, Landslide Susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches, J. Indian Soc. Remote Sens., 38, 301, 10.1007/s12524-010-0020-z Santangelo, 2011, Flood susceptibility assessment in a highly urbanized alluvial fan: the case study of Sala Consilina (southern Italy), Nat. Hazards Earth Syst. Sci., 11, 2765, 10.5194/nhess-11-2765-2011 Schreiber-Gregory, D.N. and Bader, K., 2018. Logistic and linear regression assumptions: violation recognition and control. SESUG Paper 247. [online] Available at: < https://www.researchgate.net/publication/341354759_Logistic_and_Linear_Regression_Assumptions_Violation_Recognition_and_Control >[Accessed 29 August 2021]. Schumm, 1956, Evolution of drainage systems and slopes in badlands at Perth Amboy, Geol. Soc. Am. Bull., 67, 597, 10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2 Shah-Heydari pour, 2017, Providing the fire risk map in forest area using a geographically weighted regression model with gaussian kernel and modis images, a case study: Golestan province, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W4, 2017 Tehran’s Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran, 477 Snipes, 2014, Model selection and Akaike information criteria: an example from wine ratings and prices, Wine Econ. Policy, 3, 3, 10.1016/j.wep.2014.03.001 Stat-Ease, Inc., 2021. Design-Expert v11: advanced evaluation. [online] Available at: < https://www.statease.com/docs/v11/contents/evaluation/advanced-evaluation/ >[22 April 2021]. Strahler, 1952, 63, 1117 Strahler, 1957, Quantitative analysis of watershed geomorphology, Eos Trans. Am. Geophys. Union, 38, 913, 10.1029/TR038i006p00913 Sulong, 2002, Mangrove mapping using landsat imagery and aerial photographs: Kemaman District, Terengganu, Malaysia, Environ. Dev. Sustain., 4, 135, 10.1023/A:1020844620215 Swain, 2020, Flood susceptibility mapping through the GIS-AHP technique using the cloud, ISPRS Int. J. Geo-Inf., 9, 720, 10.3390/ijgi9120720 Tali, 2011, Land use/Land cover Change and its Impact on Flood Occurrence: A Case Study of Upper Jhelum Floodplain Tehrany, 2013, Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS, J. Hydrol., 504, 69, 10.1016/j.jhydrol.2013.09.034 Tehrany, 2019, Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods, Geomat. Nat. Hazards Risk, 10, 79, 10.1080/19475705.2018.1506509 Tollan, 2002, Land-use change and floods: what do we need most, research or management?, Water Sci. Technol., 45, 183, 10.2166/wst.2002.0176 Tomaszewski, 2015, Geographic information systems for disaster response: a review, J. Homel. Secur. Emerg. Manag., 12, 571 Tran, 2020, Predicting urban waterlogging risks by regression models and internet open-data sources, Water, 12, 879, 10.3390/w12030879 Vafaei, 2018, Data normalisation techniques in decision making: case study with TOPSIS method, Int. J. Inf. Decis. Sci., 10, 19 Weibel, 2005, Generalising spatial data and dealing with multiple representations Wheeler, 2009, Geographically weighted regression, 461 Windle, 2010, Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic, ICES J. Mar. Sci., 67, 145, 10.1093/icesjms/fsp224 Wong, 2004, 571 World Health Organisation [WHO], 2020. Floods. [online] Available at: < https://www.who.int/health-topics/floods#tab=tab_1 >[Accessed 11 August 2020]. Wright, J., 2008. Chapter 2: types of floods and floodplains. [pdf] FEMA Emergency Management Institute. Available at: < https://training.fema.gov/hiedu/docs/fmc/chapter%202%20-%20types%20of%20floods%20and%20floodplains.pdf >[Accessed 23 April 2021]. Wu, 2020, The spatial non-stationary effect of urban landscape pattern on urban waterlogging: a case study of Shenzhen City, Scientific  Reports, 10 Youssef, 2011, Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery, Environ. Earth Sci., 62, 611, 10.1007/s12665-010-0551-1 Yu, 2018, Spatiotemporal variance assessment of urban rainstorm waterlogging affected by impervious surface expansion: a case study of Guangzhou, China, Sustainability, 10, 3761, 10.3390/su10103761 Yu, 2020, A landslide susceptibility map based on spatial scale segmentation: a case study at Zigui-Badong in the Three Gorges Reservoir area, China, PLoS One, 15, 10.1371/journal.pone.0229818 Yu, 2016, A combination of geographically weighted regression, particle swarm optimization and support vector machine for landslide susceptibility mapping: a case study at Wanzhou in the Three Gorges Area, China, Int. J. Environ. Res. Public Health, 13, 487, 10.3390/ijerph13050487 Zakaria, 2017, The development of flood map in Malaysia, AIP Conference Proceedings 1903, 110006 (2017); https://doi.org/10.1063/1.5011632 Published Online: 14 November 2017, 1903 Zhang, 2016, Landslide susceptibility mapping based on global and local logistic regression models in Three Gorges Reservoir area, China, Environ. Earth Sci., 75, 958, 10.1007/s12665-016-5764-5