Flooding and its relationship with land cover change,population growth,and road density |
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| Authors: | Mahfuzur Rahman Chen Ningsheng Golam Iftekhar Mahmud Md Monirul Islam Hamid Reza Pourghasemi Hilal Ahmad Jules Maurice Habumugisha Rana Muhammad Ali Washakh Mehtab Alam Enlong Liu Zheng Han Huayong Ni Tian Shufeng Ashraf Dewan |
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| Affiliation: | 1. Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment (IMHE), Chinese Academy of Sciences (CAS), Chengdu 610041, China;2. University of Chinese Academy of Sciences (UCAS), Beijing 100049, China;3. Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh;4. Academy of Plateau Science and Sustainability, Xining 810016, China;5. Development Research Initiative, Dhaka 1216, Bangladesh;6. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71454, Iran;7. School of Architecture, Neijiang Normal University, Neijiang 641100, China;8. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;9. State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China;10. School of Civil Engineering, Central South University, Changsha 410075, China;11. Chengdu Institute of Geology and Mineral Resources, China Geological Survey, China;12. School of Earth and Planetary Sciences, Curtin University, Kent St, Bentley, WA 6102, Australia |
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| Abstract: | Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks. |
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| Keywords: | Hydro-climatic disasters Machine learning algorithms Statistical model Ensemble model Relative change in flooding areas |
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