Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model

Abdollahi, 2021, Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI), Sensors, 21, 4738, 10.3390/s21144738 Arabameri, 2019, A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran, Sci. Total Environ., 660, 443, 10.1016/j.scitotenv.2019.01.021 Blum, A.G., Ferraro, P.J., Archfield, S.A., Ryberg, K.R., 2020. Causal Effect of Impervious Cover on Annual Flood Magnitude for the United States. Geophys. Res. Lett. 47(5), e2019GL086480. Botzen, 2013, Framing of risk and preferences for annual and multi-year flood insurance, J. Econ. Psychol., 39, 357, 10.1016/j.joep.2013.05.007 Chen, 2020, Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods, Sci. Total Environ., 701, 10.1016/j.scitotenv.2019.134979 Chen, 2011, Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan, Nat. Hazards, 59, 1261, 10.1007/s11069-011-9831-7 Choi, 1968, Explanatory text of the geological map of Danseong sheet, Kyeong sang nom do Choi, 1963, Explanatory text of the geological map of Uiryong sheet, Geological Survey of Korea Choubin, 2019, An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines, Sci. Total Environ., 651, 2087, 10.1016/j.scitotenv.2018.10.064 Danumah, 2016, Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques (cote d’ivoire), Geoenviron. Disasters, 3, 10, 10.1186/s40677-016-0044-y Darabi, 2019, Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques, J. Hydrol., 569, 142, 10.1016/j.jhydrol.2018.12.002 de Brito, 2016, Multi-criteria decision-making for flood risk management: a survey of the current state of the art, Nat. Hazard Earth Sys. Sci., 16, 1019, 10.5194/nhess-16-1019-2016 Dikshit, 2020, Pathways and challenges of the application of artificial intelligence to geohazards modelling, Gondwana Res Dikshit, 2020, Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia. Appl. Sci., 10, 4254, 10.3390/app10124254 Dikshit, 2021, Interpretable and Explainable AI (XAI) model for spatial drought prediction, Sci. Total Environ., 801, 10.1016/j.scitotenv.2021.149797 Dikshit, 2020, Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan, Water, 12, 267, 10.3390/w12010267 Dutta, D., Herath, S.,2004. Trend of floods in Asia and flood risk management with integrated river basin approach,. in: Proceeding of 2nd Asian Pacific Association of Hydrology and Water Resources and Conference, 55–63. Fan, 2017, Landslide susceptibility assessment using the certainty factor and analytic hierarchy process, J. Mt. Sci., 14, 906, 10.1007/s11629-016-4068-2 Fawcett, 2006, An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861, 10.1016/j.patrec.2005.10.010 Fenicia, 2014, Catchment properties, function, and conceptual model representation: is there a correspondence?, Hydrol. Process., 28, 2451, 10.1002/hyp.9726 García, 2020, Shapley additive explanations for NO2 forecasting, Ecol. Inform., 56 Gowlik, P., Tüske, Z., Schlüter, R., Ney, H.,2015. Convolutional Neural Networks for Acoustic Modeling of Raw Time Signal in LVCSR. 16th Annual Conference for the International Speech Communication Association, Germany. Hawley, 2011, How do flow peaks and durations change in suburbanizing semi-arid watersheds? A southern California case study, J. Hydrol., 405, 69, 10.1016/j.jhydrol.2011.05.011 He, 2007, Age and duration of the Emeishan flood volcanism, SW China: geochemistry and SHRIMP zircon U-Pb dating of silicic ignimbrites, post-volcanic Xuanwei Formation and clay tuff at the Chaotian section, Earth Planet. Sci. Lett., 255, 306, 10.1016/j.epsl.2006.12.021 Hong, 2018, Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution, Sci. Total Environ., 621, 1124, 10.1016/j.scitotenv.2017.10.114 Khosravi, 2020, Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran, J. Hydrol., 591, 10.1016/j.jhydrol.2020.125552 Kia, 2011, 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 Kim, S., Tachikawa, Y., Takara, K.T.,2007. Recent Flood Disasters and Progress of Disaster Management System in Korea. Kim, 1969, Explanatory text of the geologic map of JinJu sheet, Geological Survey of Korea Lecun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Lee, 2016, The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: a case study of Inje, Korea, Open Geosci., 8, 117 Lee, 2017, Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea, Geomat. Nat. Haz. Risk, 8, 1185, 10.1080/19475705.2017.1308971 Lee, 2018, Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools, Sustainability, 10, 648, 10.3390/su10030648 Lei, 2021, Urban flood modeling using deep-learning approaches in Seoul, South Korea. J. Hydrol., 601 Lundberg, S., Lee, S.-I.,2017. A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874. Luu, 2018, Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam. Nat. Hazards, 90, 1031, 10.1007/s11069-017-3083-0 Matin, 2021, Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI), Sensors, 21, 4489, 10.3390/s21134489 Meyer, 2019, Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction, Ecol. Model., 411, 10.1016/j.ecolmodel.2019.108815 Ministry of the Interior and Safety (MIS), 2019 Ministry of the Interior and Safety (MIS), 2020 Mojaddadi, 2017, Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS, Geomat. Nat. Haz. Risk, 8, 1 Molnar, 2020 Pham, 2021, Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?, J. Hydrol., 592, 10.1016/j.jhydrol.2020.125615 Rahman, 2019, Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis, Earth Syst. Environ., 3, 585, 10.1007/s41748-019-00123-y Ribeiro, M.T., Singh, S., Guestrin, C., 2016. “Why should I trust you?” Explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144. Rizeei, 2016, Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models, Geocarto Int., 31, 1158, 10.1080/10106049.2015.1120354 Rudin, 2019, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell., 1, 206, 10.1038/s42256-019-0048-x Sahoo, 2015, Development of Flood Inundation Maps and quantification of flood risk in an Urban catchment of Brahmaputra River ASCE-ASME, J. Risk Uncertain Eng. Syst., 3, A4015001 Samanta, 2018, Flood susceptibility analysis through remote sensing, GIS and frequency ratio model, Appl. Water Sci., 8, 66, 10.1007/s13201-018-0710-1 Shapley, L.S., 1953. A value for n-person games. Contributions to the Theory of Games. Vol. 2. In: Kuhn, H.W., Tucker, A.W., (Eds.), Annals of Mathematics Studies, No. 28, Princeton University. 307-317. Smith, 2013 Tehrany, 2014, Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS, J. Hydrol., 512, 332, 10.1016/j.jhydrol.2014.03.008 Tehrany, 2015, Flood susceptibility assessment using GIS-based support vector machine model with different kernel types, Catena, 125, 91, 10.1016/j.catena.2014.10.017 Vojtek, 2019, Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process, Water, 11, 364, 10.3390/w11020364 Wan, A., Dunlap, L., Ho, D., Yin, J., Lee, S., Jin, H., Petryk, S., Bargal, S.A., Gonzalez, J.E.,2020. NBDT: Neural-backed decision trees, arXiv preprint arXiv: 2004.00221. Wagenaar, 2020, Invited perspectives: how machine learning will change flood risk and impact assessmentNat, Hazards Earth Syst. Sci., 20, 1149, 10.5194/nhess-20-1149-2020 Wang, 2019, Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci. Total Environ., 666, 975, 10.1016/j.scitotenv.2019.02.263 Wang, 2020, Probabilistic stability analysis of earth dam slope under transient seepage using multivariate adaptive regression splines, Bull. Eng. Geol. Environ., 79, 2763, 10.1007/s10064-020-01730-0 Zazo, 2018, Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry, Remote Sens., 10, 1566, 10.3390/rs10101566 Zhang, 2021, Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization, Geosci. Front., 12, 469, 10.1016/j.gsf.2020.03.007 Zhang, 2022, Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China. J. Rock Mech. Geotech. Eng., 14, 1089, 10.1016/j.jrmge.2021.12.011