Current and future projections of flood risk dynamics under seasonal precipitation regimes in the Hyrcanian Forest region. Issue 25 (13th December 2022)

• Record Type: Journal Article
• Authors: Pham, Quoc Bao; Chandra Pal, Subodh; Saha, Asish; Chowdhuri, Indrajit; Albanai, Jasem A; Janizadeh, Saeid; Ahmadi, Koursoh; Khedher, Khaled Mohamed; Anh, Duong Tran; Duan, Weili
• Is Part Of: Geocarto international. Volume 37:Issue 25(2023)
• Page Start: 9047
• Page End: 9070
• Publication Date: 2022-12-13
• Subjects: Climate change -- flood risk -- machine learning -- oblique-random forest (ORF) -- Talar watershed; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.2009921
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: The main aim of this research is to predict the impact of seasonal precipitation regimes on flood hazard applying machine learning models. For this purpose, twelve static variables and eight rainfall dynamics variables for 2050s (RCP 2.6 and 8.5) were used as conditioning factors. Four machine learning algorithms including K-Nearest Neighbour (KNN), Extremely Randomize Trees (ERT), Random Forest (RF) and Oblique-Random Forest (ORF) were used to model flood risk. Considering the area under curve (AUC) and other indices, the ORF was the most optimal model. The AUC of the KNN, ERT, RF and ORF for the validation datasets were 0.85, 0.90, 0.89 and 0.92 respectively. The results showed that under two RCPs, spatial distribution of high flood risk areas will change in the future and the trends will be different from the current. These results could provide valuable insights in simulating, predicting and reducing future flood risk.