Mohammadtaghi Avand, Alban Kuriqi, Majid Khazaei, and Omid Ghorbanzadeh

Schematic representation of the study.

Schematic representation of the study.

Floods are among the devastating natural disasters that occurred very frequently in arid regions during the last decades. Accurate assessment of the flood susceptibility mapping is crucial in sustainable development. It helps respective authorities to prevent as much as possible their irreversible consequences. The Digital Elevation Model (DEM) spatial resolution is one of the most crucial base layer factors for modeling Flood Probability Maps (FPMs). Therefore, the main objective of this study was to assess the influence of the spatial resolution of the DEMs 12.5 m (ALOS PALSAR) and 30 m (ASTER) on the accuracy of flood probability prediction using three machine learning models (MLMs), including Random Forest (RF), Artificial Neural Network (ANN), and Generalized Linear Model (GLM). This study selected 14 causative factors in the flood as independent variables, and 220 flood locations were selected as dependent variables. Dependent variables were divided into training (70%) and validation (30%) for flood susceptibility modeling. The Receiver Operating Characteristic Curve (ROC), Kappa index, accuracy, and other statistical criteria were used to evaluate the models’ accuracy. The results showed that resolving the DEM alone cannot significantly affect the accuracy of flood probability prediction regardless of the applied MLM and independently of the statistical model used to assess the performance accuracy. In contrast, the factors such as altitude, precipitation, and distance from the river have a considerable impact on floods in this region. Also, the evaluation results of the models showed that the RF (AUC12.5,30m = 0.983, 0.975) model is more accurate in preparing the FPM than the ANN (AUC12.5,30m = 0.949, 0.93) and GLM (AUC12.5,30m = 0.965, 0.949) models. This study’s solution-oriented findings might help water managers and decision-makers to make the most effective adaptation and mitigation measures against potential flooding.

Journal of Hydro-environment Research, 40, 1-16, 2022-01-01.

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IARAI Authors
Omid Ghorbanzadeh
Earth Observation
Digital Elevation Model, Flood Modeling, Generalized Linear Model, Machine Learning, Neural Networks, Random Forest


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