Preparation of flood_prone areas prediction map using random forest machine learning in the Siahkhor Watershed of Kermanshah Province

Subject Areas : Sustainable Development

Ali Kiani ¹ , Baharak Motamedvaziri ² , Mohammad Reza Khaleghi ³ , Hassan Ahmadi ⁴

1 - PhD Candidate, Department of Nature Engineering, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Associate Professor, Department of Nature Engineering, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.*(Corresponding Author)
3 - Associate Professor, Department of Agriculture and Natural Resources, Torbate- Jam Branch, Islamic Azad University, Torbate- Jam, Iran.
4 - گروه سیاست دانشگاه تهران

Received: 2024-07-30 Accepted : 2025-01-08 Published : 2025-02-19

Keywords: Flood, Siahkhor Watershed, Site Selection, Machine Learning Algorithm.,

Abstract :

Background and Objective: Flash floods are considered among the most dynamic natural disasters, necessitating measures to minimize economic damages, adverse effects, and their consequences by enhancing flood sensitivity. Therefore, mapping the sensitivity of areas prone to flooding is a critical step towards flood management. Due to the scarcity of information in most of the country's watershed areas, many researchers rely on spatial analyses for hydrological studies and flood mapping. Consequently, identifying the most significant factors influencing the creation and intensification of floods, as well as mapping their sensitivity, can be one of the most important strategies for reducing flood risk. In the current study, the Siah Khor watershed in the northeast of Islam Abad-e Gharb (Kermanshah province) was selected for predicting flood-prone areas and identifying key factors affecting their occurrence.

Material and Methodology: To identify flood-prone areas in this study region, the Random Forest machine learning (ML) algorithm was employed within a Python environment. A map depicting the distribution of past flood events was created to predict future flooding.

Findings: Fifty-three flood events were recorded in the area, and forty-nine regions were identified as non-flood zones; 70% of the data was used for modeling and 30% for validation purposes. After reviewing previous studies and surveying the study area, eight influential factors (slope, drainage density, land use, distance from the river, geological characteristics, rainfall, slope direction, and elevation) were selected for flood zoning. The Relative Operational Characteristic (ROC) curve was utilized for model validation and efficiency evaluation.

Predicting Flood-Prone Areas Using Machine Learning Methods in the Siahkhor Watershed of Kermanshah Province

Discussion and Conclusion: The results indicated that among the eight factors affecting flood zones, rainfall (0.35), distance from the river (0.27), and elevation (0.21) have the most significant impact on flooding in the study area, respectively. Furthermore, the evaluation of model outputs revealed that the Area Under the Curve (AUC) value for the Random Forest (RF) model was 0.98, demonstrating the high efficiency and accuracy of the RF model in mapping flood sensitivity in the study area. The largest area of flood sensitivity in the RF model corresponds to the very low category. The findings suggest that employing the Random Forest machine learning (ML) algorithm can be effectively used in analyzing flood risk.

 

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