Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in transboundary river basin

References

• Acharya, A., and A. Prakash. 2019. “When the River Talks to its People: Local Knowledge-Based Flood Forecasting in Gandak River Basin, India.” Environmental Development 31 (July): 55–67. https://doi.org/10.1016/j.envdev.2018.12.003.
   Google Scholar
• Adhikari, B. R. 2013. “Flooding and Inundation in Nepal Terai: Issues and Concerns.” Hydro Nepal: Journal of Water, Energy and Environment 12: 59–65. https://doi.org/10.3126/hn.v12i0.9034.
   Google Scholar
• Aldiansyah, S., and F. Wardani. 2023. “Evaluation of Flood Susceptibility Prediction Based on a Resampling Method Using Machine Learning.” Journal of Water and Climate Change 14 (3): 937–961. https://doi.org/10.2166/wcc.2023.494.
   Web of Science ®Google Scholar
• Ali, S. A., F. Parvin, Q. B. Pham, M. Vojtek, J. Vojteková, R. Costache, N. T. T. Linh, H. Q. Nguyen, A. Ahmad, and M. A. Ghorbani. 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.” Ecological Indicators 117 (December 2019): 106620. https://doi.org/10.1016/j.ecolind.2020.106620.
   Google Scholar
• Andaryani, S., V. Nourani, A. T. Haghighi, and S. Keesstra. 2021. “Integration of Hard and Soft Supervised Machine Learning for Flood Susceptibility Mapping.” Journal of Environmental Management 291 (April): 112731.
   PubMedGoogle Scholar
• Antzoulatos, G., I. O. Kouloglou, M. Bakratsas, A. Moumtzidou, I. Gialampoukidis, A. Karakostas, F. Lombardo, R. Fiorin, D. Norbiato, M. Ferri, et al. 2022. “Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data.” Sustainability 14 (6). https://doi.org/10.3390/su14063251.
   Web of Science ®Google Scholar
• Anzellini, Vicente, Christelle Cazabat, Cortes Fernandes Pablo, Fai-Dutra Santos, Vincent Fund, Kathryn Giffin, Thannaletchimy Housset, Alesia O'Connor, Fanny Teppe, and Louisa Yasukawa. 2022. Disaster Displacement in Asia and the Pacific: A Business Case for Investment in Prevention and Solutions in Asia and the Pacific Disaster Displacement [Internet]. Geneva. Accessed July 28, 2023. http://internal-displacement.org.
   Google Scholar
• Arora, A., A. Arabameri, M. Pandey, M. A. Siddiqui, U. K. Shukla, D. T. Bui, V. N. Mishra, and A. Bhardwaj. 2021. “Optimization of State-of-the-art Fuzzy-Metaheuristic ANFIS-Based Machine Learning Models for Flood Susceptibility Prediction Mapping in the Middle Ganga Plain, India.” Science of the Total Environment 750. https://doi.org/10.1016/j.scitotenv.2020.141565.
   PubMed Web of Science ®Google Scholar
• Avand, M., A. Kuriqi, M. Khazaei, and O. Ghorbanzadeh. 2022a. “DEM Resolution Effects on Machine Learning Performance for Flood Probability Mapping.” Journal of Hydro-Environment Research 40: 1–16. https://doi.org/10.1016/j.jher.2021.10.002.
   Web of Science ®Google Scholar
• Avand, M., H. Moradi, and M. Ramazanzadeh lasboyee. 2022b. “Predicting Temporal and Spatial Variability in Flood Vulnerability and Risk of Rural Communities at the Watershed Scale.” Journal of Environmental Management 323: 116261. https://doi.org/10.1016/j.jenvman.2022.116261.
   PubMed Web of Science ®Google Scholar
• Bajracharya, T. R., S. Acharya, and B. B. Ale. 1970. “Changing Climatic Parameters and its Possible Impacts in Hydropower Generation in Nepal (A Case Study on Gandaki River Basin).” Journal of the Institute of Engineering 8 (1–2): 160–173. https://doi.org/10.3126/jie.v8i1-2.5108.
   Google Scholar
• Bakker, M. H. N. 2009, June 1. “Transboundary River Floods: Examining Countries, International River Basins and Continents.” Water Policy 11 (3): 269–288. https://doi.org/10.2166/wp.2009.041.
   Web of Science ®Google Scholar
• Brakenridge, G. R. 2016. Global Active Archive of Large Flood Events. Dartmouth Flood Observatory, University of Colorado, USA. http://floodobservatory.colorado.edu/Archives/ (Accessed 1 October 2016).
   Google Scholar
• Bui, D. T., K. Khosravi, S. Li, H. Shahabi, M. Panahi, V. P. Singh, K. Chapi, et al. 2018a. “New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling.” Water 10 (9). https://doi.org/10.3390/w10091210.
   Web of Science ®Google Scholar
• Bui, D. T., M. Panahi, H. Shahabi, V. P. Singh, A. Shirzadi, K. Chapi, K. Khosravi, et al. 2018b. “Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods.” Scientific Reports 8 (1): 1–14. https://doi.org/10.1038/s41598-018-33755-7.
   PubMedGoogle Scholar
• Bui, D. T., B. Pradhan, I. Revhaug, D. B. Nguyen, H. V. Pham, and Q. N. Bui. 2015. “A Novel Hybrid Evidential Belief Function-Based Fuzzy Logic Model in Spatial Prediction of Rainfall-Induced Shallow Landslides in the Lang Son City Area (Vietnam).” Geomatics, Natural Hazards and Risk 6 (3): 243–271. https://doi.org/10.1080/19475705.2013.843206.
   Web of Science ®Google Scholar
• Cao, C., P. Xu, Y. Wang, J. Chen, L. Zheng, and C. Niu. 2016. “Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas.” Sustainability 8 (9). https://doi.org/10.3390/su8090948.
   PubMed Web of Science ®Google Scholar
• Chapi, K., V. P. Singh, A. Shirzadi, H. Shahabi, D. T. Bui, B. T. Pham, and K. Khosravi. 2017. “A Novel Hybrid Artificial Intelligence Approach for Flood Susceptibility Assessment.” Environmental Modelling & Software 95: 229–245. https://doi.org/10.1016/j.envsoft.2017.06.012.
   Web of Science ®Google Scholar
• Chen, W., H. Hong, S. Li, H. Shahabi, Y. Wang, X. Wang, and B Bin Ahmad. 2019. “Flood Susceptibility Modelling Using Novel Hybrid Approach of Reduced-Error Pruning Trees with Bagging and Random Subspace Ensembles.” Journal of Hydrology 575 (February): 864–873. https://doi.org/10.1016/j.jhydrol.2019.05.089.
   Google Scholar
• Chen, J., Y. Li, C. Zhang, Y. Tian, and Z. Guo. 2023. “Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model.” International Journal of Environmental Research and Public Health 20 (2). https://doi.org/10.3390/ijerph20021043.
   Web of Science ®Google Scholar
• Choubin, B., E. Moradi, M. Golshan, J. Adamowski, F. Sajedi-Hosseini, and A. Mosavi. 2019. “An Ensemble Prediction of Flood Susceptibility Using Multivariate Discriminant Analysis, Classification and Regression Trees, and Support Vector Machines.” Science of The Total Environment 651: 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064.
   PubMed Web of Science ®Google Scholar
• Cooley, H., and P. H. Gleick. 2011. “Protéger les Accords sur les Eaux Transfrontalières Contre le Climat.” Hydrological Sciences Journal 56 (4): 711–718. https://doi.org/10.1080/02626667.2011.576651.
   Web of Science ®Google Scholar
• Costache, R., Q. B. Pham, E. Sharifi, N. T. T. Linh, S. I. Abba, M. Vojtek, J. Vojteková, P. T. T. Nhi, and D. N. Khoi. 2020. “Flash-flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques.” Remote Sensing 12 (1). https://doi.org/10.3390/RS12010106.
   Web of Science ®Google Scholar
• Costache, R., and D. Tien Bui. 2020. “Identification of Areas Prone to Flash-Flood Phenomena Using Multiple-Criteria Decision-Making, Bivariate Statistics, Machine Learning and Their Ensembles.” Science of The Total Environment 712: 136492. https://doi.org/10.1016/j.scitotenv.2019.136492.
   PubMed Web of Science ®Google Scholar
• Dandekhya, S., M. England, R. Ghate, C. G. Goodrich, S. Nepal, A. Prakash, A. Shrestha, S. Singh, M. S. Singh, and P. B. Udas. 2017. The Gandaki Basin Maintaining Livelihoods in the Face of Landslides, Floods, and Drought A ARE HI Himalayan Adaptation, Water and Resilience Research. www.hi-aware.org.
   Google Scholar
• Dekongmen, B. W., A. T. Kabo-bah, M. K. Domfeh, E. D. Sunkari, Y. T. Dile, E. O. Antwi, and R. A. A. Gyimah. 2021. “Flood Vulnerability Assessment in the Accra Metropolis, Southeastern Ghana.” Applied Water Science 11 (7). https://doi.org/10.1007/s13201-021-01463-9.
   Web of Science ®Google Scholar
• Devitt, L., J. Neal, G. Coxon, J. Savage, and T. Wagener. 2023. “Flood Hazard Potential Reveals Global Floodplain Settlement Patterns.” Nature Communications 14 (1). https://doi.org/10.1038/s41467-023-38297-9.
   PubMed Web of Science ®Google Scholar
• Dhungel, D. N., and S. B. Pun. 2009. The Nepal-India Water Relationship : Challenges. [place unknown]: Springer.
   Google Scholar
• Dixit, A. S. A. 2017. Gandak River Agreement.
   Google Scholar
• Dixit, A., and A. Shukla. 2022. Benefits and Burden A Case Study of Gandak River Agreement. New York: Routledge.
   Google Scholar
• Dodangeh, E., M. Panahi, F. Rezaie, S. Lee, D. Tien Bui, C. W. Lee, and B. Pradhan. 2020. “Novel Hybrid Intelligence Models for Flood-Susceptibility Prediction: Meta Optimization of the GMDH and SVR Models with the Genetic Algorithm and Harmony Search.” Journal of Hydrology 590 (July): 125423. https://doi.org/10.1016/j.jhydrol.2020.125423.
   Google Scholar
• Dtissibe, F. Y., A. A. A. Ari, C. Titouna, O. Thiare, and A. M. Gueroui. 2020. “Flood Forecasting Based on an Artificial Neural Network Scheme.” Natural Hazards 104 (2): 1211–1237. https://doi.org/10.1007/s11069-020-04211-5.
   Web of Science ®Google Scholar
• Dutta, Venkatesh. 2022. Water Conflicts and Resistance; Issues and Challenges in South Asia. New York.
   Google Scholar
• Duwal, S., D. Liu, and P. M. Pradhan. 2023. “Flood Susceptibility Modeling of the Karnali River Basin of Nepal Using Different Machine Learning Approaches.” Geomatics, Natural Hazards and Risk 14 (1). https://doi.org/10.1080/19475705.2023.2217321.
   PubMed Web of Science ®Google Scholar
• Fang, Z., Y. Wang, L. Peng, and H. Hong. 2021. “Predicting Flood Susceptibility Using LSTM Neural Networks.” Journal of Hydrology 594. https://doi.org/10.1016/j.jhydrol.2020.125734.
   PubMed Web of Science ®Google Scholar
• FAO. 2012. HWSD. Harmonized World Soil Database (version 12).
   Google Scholar
• GeoNet Connection Pvt. Ltd. 2019. Feasibility Study of Disaster Risk Area Identification and Scientific Mapping (HAZARD) in Gandaki Province, Nepal. Pokhara.
   Google Scholar
• Ghosh, S., S. Saha, and B. Bera. 2022. “Flood Susceptibility Zonation Using Advanced Ensemble Machine Learning Models Within Himalayan Foreland Basin.” Natural Hazards Research 2 (4): 363–374. https://doi.org/10.1016/j.nhres.2022.06.003.
   Google Scholar
• Gudiyangada Nachappa, T., S. Tavakkoli Piralilou, K. Gholamnia, O. Ghorbanzadeh, O. Rahmati, and T. Blaschke. 2020. “Flood Susceptibility Mapping with Machine Learning, Multi-Criteria Decision Analysis and Ensemble Using Dempster Shafer Theory.” Journal of Hydrology 590: 125275. https://doi.org/10.1016/j.jhydrol.2020.125275.
   Web of Science ®Google Scholar
• Gupta, N., S. Dahal, A. Kumar, C. Kumar, M. Kumar, A. Maharjan, D. Mishra, A. Mohanty, A. Navaraj, S. Pandey, et al. 2021. “Rich Water, Poor People: Potential for Transboundary Flood Management Between Nepal and India.” Current Research in Environmental Sustainability 3. https://doi.org/10.1016/j.crsust.2021.100031.
   Google Scholar
• Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, S. Watanabe, H. Kim, and S. Kanae. 2013. “Global Flood Risk Under Climate Change.” Nature Climate Change 3 (9): 816–821. https://doi.org/10.1038/nclimate1911.
   Web of Science ®Google Scholar
• Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Horton, R. E. 1945. “Erosional Development of Streams and Their Drainage Basins; Hydrophysical Approach to Quantitative Morphology.” Geological Society of America Bulletin 56 (3): 275–370. https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2.
   Web of Science ®Google Scholar
• Huffman, G. DBDBKHRJPX. 2015. Integrated Multi-satellite Retrievals for GPM (IMERG). NASA’s Precipitation Processing Center.
   Google Scholar
• Ignacio, K. J., and D. E. Walling. 1968. “Time Series Analyses of Water and Sediment Discharges.” International Association of Scientific Hydrology Bulletin 13 (2): 69–84. https://doi.org/10.1080/02626666809493583.
   Google Scholar
• JICA. 1999. https://openjicareport.jica.go.jp/pdf/11505302_01.pdf.
   Google Scholar
• Karra, K., C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis, and S. P. Brumby. 2021. “Global Land use / Land Cover with Sentinel 2 and Deep Learning.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–4707. [place unknown]. https://doi.org/10.1109/IGARSS47720.2021.9553499.
   Google Scholar
• Khanal, N. R., P. K. Mool, A. B. Shrestha, G. Rasul, P. K. Ghimire, R. B. Shrestha, and S. P. Joshi. 2015. “A Comprehensive Approach and Methods for Glacial Lake Outburst Flood Risk Assessment, with Examples from Nepal and the Transboundary Area.” International Journal of Water Resources Development 31 (2): 219–237. https://doi.org/10.1080/07900627.2014.994116.
   Web of Science ®Google Scholar
• Khosravi, K., E. Nohani, E. Maroufinia, and H. R. Pourghasemi. 2016. “A GIS-Based Flood Susceptibility Assessment and its Mapping in Iran: A Comparison Between Frequency Ratio and Weights-of-Evidence Bivariate Statistical Models with Multi-Criteria Decision-Making Technique.” Natural Hazards 83 (2): 947–987. https://doi.org/10.1007/s11069-016-2357-2.
   Web of Science ®Google Scholar
• Khosravi, K., M. Panahi, A. Golkarian, S. D. Keesstra, P. M. Saco, D. T. Bui, and S. Lee. 2020. “Convolutional Neural Network Approach for Spatial Prediction of Flood Hazard at National Scale of Iran.” Journal of Hydrology 591: 125552. https://doi.org/10.1016/j.jhydrol.2020.125552.
   Web of Science ®Google Scholar
• Khosravi, K., H. Shahabi, B. T. Pham, J. Adamowski, A. Shirzadi, B. Pradhan, J. Dou, H. B. Ly, G. Gróf, H. L. Ho, et al. 2019. “A Comparative Assessment of Flood Susceptibility Modeling Using Multi-Criteria Decision-Making Analysis and Machine Learning Methods.” Journal of Hydrology 573: 311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073.
   Web of Science ®Google Scholar
• Liu, M., Y. Huang, Z. Li, B. Tong, Z. Liu, M. Sun, F. Jiang, and H. Zhang. 2020. “The Applicability of Lstm-knn Model for Real-Time Flood Forecasting in Different Climate Zones in China.” Water 12 (2). https://doi.org/10.3390/w12020440.
   Web of Science ®Google Scholar
• Luu, C., B. T. Pham, T Van Phong, R. Costache, H. D. Nguyen, M. Amiri, Q. D. Bui, et al. 2021. “GIS-based Ensemble Computational Models for Flood Susceptibility Prediction in the Quang Binh Province, Vietnam.” Journal of Hydrology 599 (November 2020): 126500. https://doi.org/10.1016/j.jhydrol.2021.126500.
   Google Scholar
• Mashao, F. M., M. C. Mothapo, R. B. Munyai, J. M. Letsoalo, I. L. Mbokodo, T. P. Muofhe, W. Matsane, and H. Chikoore. 2023. “Extreme Rainfall and Flood Risk Prediction Over the East Coast of South Africa.” Water 15 (1). https://doi.org/10.3390/w15010050.
   Web of Science ®Google Scholar
• Mehravar, S., S. V. Razavi-Termeh, A. Moghimi, B. Ranjgar, F. Foroughnia, and M. Amani. 2023. “Flood Susceptibility Mapping Using Multi-Temporal SAR Imagery and Novel Integration of Nature-Inspired Algorithms Into Support Vector Regression.” Journal of Hydrology 617. https://doi.org/10.1016/j.jhydrol.2023.129100.
   PubMed Web of Science ®Google Scholar
• Merz, B., G. Blöschl, S. Vorogushyn, F. Dottori, J. C. J. H. Aerts, P. Bates, M. Bertola, et al. 2021. “Causes, Impacts and Patterns of Disastrous River Floods.” Nature Reviews Earth & Environment 2 (9): 592–609. https://doi.org/10.1038/s43017-021-00195-3.
   Google Scholar
• Munasinghe, D., S. Cohen, Y. F. Huang, Y. P. Tsang, J. Zhang, and Z. Fang. 2018. “Intercomparison of Satellite Remote Sensing-Based Flood Inundation Mapping Techniques.” JAWRA Journal of the American Water Resources Association 54 (4): 834–846. https://doi.org/10.1111/1752-1688.12626.
   Web of Science ®Google Scholar
• Munia, H. A., J. H. A. Guillaume, Y. Wada, T. Veldkamp, V. Virkki, and M. Kummu. 2020. “Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study.” Earths Future 8 (7). https://doi.org/10.1029/2019EF001321.
   PubMed Web of Science ®Google Scholar
• Nandi, I., P. K. Srivastava, and K. Shah. 2017. “Floodplain Mapping Through Support Vector Machine and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi.” Water Resources Management 31 (4): 1157–1171. https://doi.org/10.1007/s11269-017-1568-y.
   Web of Science ®Google Scholar
• NASA JPL. 2013. NASA Shuttle Radar Topography Mission Global 1 arc second [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2024-02-02 from https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003.
   Google Scholar
• Pallard, B., A. Castellarin, and A. Montanari. 2009. Hydrology and Earth System Sciences A look at the links between drainage density and flood statistics. [place unknown]. www.hydrol-earth-syst-sci.net/13/1019/2009/.
   Google Scholar
• Panahi, M., O. Rahmati, Z. Kalantari, H. Darabi, F. Rezaie, D. D. Moghaddam, C. S. S. Ferreira, G. Foody, R. Aliramaee, S. M. Bateni, et al. 2022. “Large-scale Dynamic Flood Monitoring in an Arid-Zone Floodplain Using SAR Data and Hybrid Machine-Learning Models.” Journal of Hydrology 611. https://doi.org/10.1016/j.jhydrol.2022.128001.
   Web of Science ®Google Scholar
• Pandey, C. L., J. Joseph, R. Deshar, and P. Niraula. 2023. “Transboundary Flood Resilience: Insights from Narayani and Mahakali Basins.” International Journal of Disaster Risk Reduction 86. https://doi.org/10.1016/j.ijdrr.2023.103535.
   Web of Science ®Google Scholar
• Panthi, J., P. Dahal, M. L. Shrestha, S. Aryal, N. Y. Krakauer, S. M. Pradhanang, T. Lakhankar, A. K. Jha, M. Sharma, and R. Karki. 2015. “Spatial and Temporal Variability of Rainfall in the Gandaki River Basin of Nepal Himalaya.” Climate 3 (1): 210–226. https://doi.org/10.3390/cli3010210.
   Web of Science ®Google Scholar
• Pekel, J. F., A. Cottam, N. Gorelick, and A. S. Belward. 2016. “High-resolution Mapping of Global Surface Water and its Long-Term Changes.” Nature 540 (7633): 418–422. https://doi.org/10.1038/nature20584.
   PubMed Web of Science ®Google Scholar
• Pham, B. T., A. Jaafari, Phong T Van, H. P. H. Yen, T. T. Tuyen, Luong V Van, H. D. Nguyen, Le H Van, and L. K. Foong. 2021. “Improved Flood Susceptibility Mapping Using a Best First Decision Tree Integrated with Ensemble Learning Techniques.” Geoscience Frontiers 12 (3). https://doi.org/10.1016/j.gsf.2020.11.003.
   Web of Science ®Google Scholar
• Pourghasemi, H. R., B. Pradhan, and C. Gokceoglu. 2012. “Application of Fuzzy Logic and Analytical Hierarchy Process (AHP) to Landslide Susceptibility Mapping at Haraz Watershed, Iran.” Natural Hazards 63 (2): 965–996. https://doi.org/10.1007/s11069-012-0217-2.
   Web of Science ®Google Scholar
• Rentschler, J., M. Salhab, and B. A. Jafino. 2022. “Flood Exposure and Poverty in 188 Countries.” Nature Communications 13 (1): 1–11. https://doi.org/10.1038/s41467-022-30727-4.
   PubMed Web of Science ®Google Scholar
• Riazi, M., K. Khosravi, K. Shahedi, S. Ahmad, C. Jun, S. Bateni, and N. Kazakis. 2023. “Enhancing Flood Susceptibility Modeling Using Multi-Temporal SAR Images, CHIRPS Data, and Hybrid Machine Learning Algorithms.” Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2023.162066.
   PubMed Web of Science ®Google Scholar
• Richman, R., and M. V. Wüthrich. 2020. “Nagging Predictors.” Risks 8 (3): 1–26. https://doi.org/10.3390/risks8030083.
   Web of Science ®Google Scholar
• Saber, M., T. Boulmaiz, M. Guermoui, K. I. Abdrabo, S. A. Kantoush, T. Sumi, H. Boutaghane, T. Hori, Binh D Van, B. Q. Nguyen, et al. 2023. “Enhancing Flood Risk Assessment Through Integration of Ensemble Learning Approaches and Physical-Based Hydrological Modeling.” Geomatics, Natural Hazards and Risk 14 (1). https://doi.org/10.1080/19475705.2023.2203798.
   PubMed Web of Science ®Google Scholar
• Schmidt, L., F. Heße, S. Attinger, and R. Kumar. 2020. “Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany.” Water Resources Research 56 (5). https://doi.org/10.1029/2019WR025924.
   Web of Science ®Google Scholar
• Schoppa, L., M. Disse, and S. Bachmair. 2020. “Evaluating the Performance of Random Forest for Large-Scale Flood Discharge Simulation.” Journal of Hydrology 590 (August): 125531. https://doi.org/10.1016/j.jhydrol.2020.125531.
   Google Scholar
• Sentinel. 2013. SENTINEL-2 User Handbook Sentinel-2 User Handbook SENTINEL-2 User Handbook Title Sentinel-2 User Handbook Issue 1 Revision 1 SENTINEL-2 User Handbook. [place unknown].
   Google Scholar
• Seydi, S. T., Y. Kanani-Sadat, M. Hasanlou, R. Sahraei, J. Chanussot, and M. Amani. 2023. “Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping.” Remote Sensing 15 (1). https://doi.org/10.3390/rs15010192.
   PubMed Web of Science ®Google Scholar
• Shafapour Tehrany, M., L. Kumar, M. Neamah Jebur, and F. Shabani. 2019. “Evaluating the Application of the Statistical Index Method in Flood Susceptibility Mapping and its Comparison with Frequency Ratio and Logistic Regression Methods.” Geomatics, Natural Hazards and Risk 10 (1): 79–101. https://doi.org/10.1080/19475705.2018.1506509.
   Web of Science ®Google Scholar
• Shafizadeh-Moghadam, H., R. Valavi, H. Shahabi, K. Chapi, and A. Shirzadi. 2018. “Novel Forecasting Approaches Using Combination of Machine Learning and Statistical Models for Flood Susceptibility Mapping.” Journal of Environmental Management 217: 1–11. https://doi.org/10.1016/j.jenvman.2018.03.089.
   PubMed Web of Science ®Google Scholar
• Sharma, A. P., X. Fu, and G. R. Kattel. 2023. “Is There a Progressive Flood Risk Management in Nepal? A Synthesis Based on the Perspective of a Half-Century (1971–2020) Flood Outlook.” Natural Hazards, https://doi.org/10.1007/s11069-023-06035-5.
   PubMed Web of Science ®Google Scholar
• Sherstinsky, A. 2020. “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network.” Physica D: Nonlinear Phenomena 404. https://doi.org/10.1016/j.physd.2019.132306.
   Web of Science ®Google Scholar
• Shrestha, A. B., C. P. Wake, P. A. Mayewski, and J. E. Dibb. 1999. “Maximum Temperature Trends in the Himalaya and its Vicinity: An Analysis Based on Temperature Records from Nepal for the Period 1971-94.” Journal of Climate 12 (9): 2775–2786. https://doi.org/10.1175/1520-0442(1999)012<2775:MTTITH>2.0.CO;2.
   Web of Science ®Google Scholar
• Tazmul Islam, M., and Q. Meng. 2022. “An Exploratory Study of Sentinel-1 SAR for Rapid Urban Flood Mapping on Google Earth Engine.” International Journal of Applied Earth Observation and Geoinformation 113. https://doi.org/10.1016/j.jag.2022.103002.
   Web of Science ®Google Scholar
• Tehrany, M. S., S. Jones, and F. Shabani. 2019. “Identifying the Essential Flood Conditioning Factors for Flood Prone Area Mapping Using Machine Learning Techniques.” CATENA 175 (April 2018): 174–192. https://doi.org/10.1016/j.catena.2018.12.011.
   Google Scholar
• Tehrany, M. S., B. Pradhan, and M. N. Jebur. 2014. “Flood Susceptibility Mapping Using a Novel Ensemble Weights-of-Evidence and Support Vector Machine Models in GIS.” Journal of Hydrology 512: 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008.
   Web of Science ®Google Scholar
• Tehrany, M. S., B. Pradhan, and M. N. Jebur. 2015. “Flood Susceptibility Analysis and its Verification Using a Novel Ensemble Support Vector Machine and Frequency Ratio Method.” Stochastic Environmental Research and Risk Assessment 29 (4): 1149–1165. https://doi.org/10.1007/s00477-015-1021-9.
   Web of Science ®Google Scholar
• Tehrany, M. S., B. Pradhan, S. Mansor, and N. Ahmad. 2015. “Flood Susceptibility Assessment Using GIS-Based Support Vector Machine Model with Different Kernel Types.” CATENA 125: 91–101. https://doi.org/10.1016/j.catena.2014.10.017.
   Web of Science ®Google Scholar
• Towfiqul Islam, A. R. M., S. Talukdar, S. Mahato, S. Kundu, K. U. Eibek, Q. B. Pham, A. Kuriqi, and N. T. T. Linh. 2021. “Flood Susceptibility Modelling Using Advanced Ensemble Machine Learning Models.” Geoscience Frontiers 12 (3). https://doi.org/10.1016/j.gsf.2020.09.006.
   Web of Science ®Google Scholar
• Wang, Y., Z. Fang, H. Hong, and L. Peng. 2020. “Flood Susceptibility Mapping Using Convolutional Neural Network Frameworks.” Journal of Hydrology 582: 124482. https://doi.org/10.1016/j.jhydrol.2019.124482.
   Web of Science ®Google Scholar
• Winsemius, H. C., J. C. J. H. Aerts, L. P. H. Van Beek, M. F. P. Bierkens, A. Bouwman, B. Jongman, J. C. J. Kwadijk, et al. 2016. “Global Drivers of Future River Flood Risk.” Nature Climate Change 6 (4): 381–385. https://doi.org/10.1038/nclimate2893.
   Web of Science ®Google Scholar
• Yin, J., P. Gentine, S. Zhou, S. C. Sullivan, R. Wang, Y. Zhang, and S. Guo. 2018. “Large Increase in Global Storm Runoff Extremes Driven by Climate and Anthropogenic Changes.” Nature Communications 9 (1). https://doi.org/10.1038/s41467-018-06765-2.
   Google Scholar
• Youssef, A. M., B. Pradhan, A. Dikshit, and A. M. Mahdi. 2022. “Comparative Study of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Flood Susceptibility Mapping: A Case Study at Ras Gharib, Red Sea, Egypt.” Geocarto International 37 (26): 11088–11115.
   Web of Science ®Google Scholar
• Zheng, G., S. K. Allen, A. Bao, J. A. Ballesteros-Cánovas, M. Huss, G. Zhang, J. Li, Y. Yuan, L. Jiang, T. Yu, et al. 2021. “Increasing Risk of Glacial Lake Outburst Floods from Future Third Pole Deglaciation.” Nature Climate Change 11 (5): 411–417. https://doi.org/10.1038/s41558-021-01028-3.
   Web of Science ®Google Scholar
• Zhou, Y., W. Wu, R. Nathan, and Q. J. Wang. 2022. “Deep Learning-Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths.” Water Resources Research 58 (12). https://doi.org/10.1029/2022WR033214.
   Web of Science ®Google Scholar