GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naive Bayes tree, bivariate statistics and logistic regression: A case of Topla basin, Slovakia

被引:202
作者
Ali, Sk Ajim [1 ]
Parvin, Farhana [1 ]
Quoc Bao Pham [2 ,3 ]
Vojtek, Matej [4 ]
Vojtekova, Jana [4 ]
Costache, Romulus [5 ,6 ]
Nguyen Thi Thuy Linh [7 ]
Hong Quan Nguyen [8 ,9 ]
Ahmad, Ateeque [1 ]
Ghorbani, Mohammad Ali [10 ]
机构
[1] Aligarh Muslim Univ AMU, Dept Geog, Fac Sci, Aligarh 202002, Uttar Pradesh, India
[2] Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam
[3] Duy Tan Univ, Fac Environm & Chem Engn, Danang 550000, Vietnam
[4] Constantine Philosopher Univ Nitra, Fac Nat Sci, Dept Geog & Reg Dev, Trieda A Hlinku 1, Nitra 94974, Slovakia
[5] Univ Bucharest, Res Inst, 90-92 Sos Panduri,5th Dist, Bucharest 050663, Romania
[6] Natl Inst Hydrol & Water Management, Bucuresti Ploiesti Rd,97E,1st Dist, Bucharest 013686, Romania
[7] Thuyloi Univ, 175 Tay Son, Hanoi, Vietnam
[8] Vietnam Natl Univ, Inst Environm & Resources, Ctr Water Management & Climate Change, Ho Chi Minh City 700000, Vietnam
[9] Vietnam Natl Univ Ho Chi Minh City, Inst Circular Econ Dev ICED, Ho Chi Minh City, Vietnam
[10] Ton Duc Thang Univ, Fac Environm & Labour Safety, Sustainable Management Nat Resources & Environm R, Ho Chi Minh City, Vietnam
关键词
Hybrid multi-criteria approach; Bivariate statistics; Machine learning; Flood susceptibility; Topla river basin; Slovakia; GIS; ANALYTICAL HIERARCHY PROCESS; FREQUENCY RATIO MODEL; REMOTE-SENSING DATA; WEIGHTS-OF-EVIDENCE; LAST; 5; DECADES; RIVER-BASIN; VULNERABILITY ASSESSMENT; SPATIAL PREDICTION; ARTIFICIAL-INTELLIGENCE; CURVE NUMBER;
D O I
10.1016/j.ecolind.2020.106620
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying the susceptible areas to flood is an important task for every country to prevent such dangerous consequences. The present study developed a framework for identifying flood-prone areas of the Topla river basin, Slovakia using geographic information system (GIS), multi-criteria decision making approach (MCDMA), bivariate statistics (Frequency Ratio (FR), Statistical Index (SI)) and machine learning (Naive Bayes Tree (NBT), Logistic Regression (LR)). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. To access the relationship and interdependences among the criteria, decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) were used. Based on the experts' decisions, the DEMATEL-ANP model was used to compute the relative weights of different criteria and a GIS-based linear combination was performed to derive the susceptibility index. Separately, the flood susceptibility index computation through NBT-FR and NBT-SI hybrid models assumed, in the first stage, the estimation of the weight of each class/category of conditioning factor through SI and FR and the integration of these values in NBT algorithm. The application of LR stand-alone required the calculation of the weights of conditioning factors by analysing their spatial relation with the location of the historical flood events. The study revealed that very high and high flood susceptibility classes covered between 20% and 47% of the study area, respectively. The validation of results, using the past flood points, highlighted that the hybrid DEMATEL-ANP model was the most performant with an Area Under ROC curve higher than 0.97, an accuracy of 0.922 and a value of HSS of 0.844. The presented methodological approach used for the identification of flood susceptible areas can serve as an alternative for the updating of preliminary flood risk assessment based on the EU Floods Directive.
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页数:23
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