Detection of areas prone to flood risk using state-of-the-art machine learning models. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Costache, Romulus; Arabameri, Alireza; Elkhrachy, Ismail; Ghorbanzadeh, Omid; Pham, Quoc Bao
• Is Part Of: Geomatics, natural hazards & risk. Volume 12:Issue 1(2021)
• Page Start: 1488
• Page End: 1507
• Publication Date: 2021-01-01
• Subjects: Buzău catchment -- flood susceptibility -- machine learning -- Romania -- GIS; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2021.1920480
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract: The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC = 0.996).