Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Vojtek, Matej; Vojteková, Jana; Costache, Romulus; Pham, Quoc Bao; Lee, Sunmin; Arshad, Arfan; Sahoo, Satiprasad; Linh, Nguyen Thi Thuy; Anh, Duong Tran
• Is Part Of: Geomatics, natural hazards & risk. Volume 12:Issue 1(2021)
• Page Start: 1153
• Page End: 1180
• Publication Date: 2021-01-01
• Subjects: Flood susceptibility -- multi-criteria decision analysis -- analytical hierarchy process -- machine learning -- boosting -- decision trees -- GIS -- Slovakia; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2021.1912835
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract: Identification of areas susceptible to floods is an important issue which requires an increased attention due to the changing frequency and magnitude of floods, which is mainly a result of the ongoing climate change and increasing anthropic pressure on the landscape. In this study, the aim was to identify the areas susceptible to floods using and comparing two different approaches, namely the multi-criteria decision analysis-analytical hierarchy process (MCDA-AHP) and the machine learning-boosted classification (BCT) and boosted regression (BRT) tree. The study area was represented by the Topľa river basin, Slovakia. Altogether, seven relevant flood conditioning factors: elevation, slope, river network density, distance from river, flow accumulation, curve numbers and lithology as well as flood inventory database consisting of 107 flood locations were used. Based on the results, almost 40% of the study area is characterized by high to very high flood susceptibility using the MCDA-AHP. In case of the BCT and BRT models, the share of high and very high flood susceptibility class on the basin area is 45% and 38%, respectively. Validation of the performed flood susceptibility models confirmed generally higher accuracy of the machine learning models. The accuracy of the MCDA-AHP model was 81.33% while the accuracy of the boosted tree models was 87.70% and 91.42%, respectively, for classification and regression. The results of this study can enhance more effective preliminary flood risk assessment according to the EU Floods Directive.