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Received: 2024/02/10 | Accepted: 2024/04/17
Received: 2024/02/10 | Accepted: 2024/04/17
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1- Hakim Sabzevari University
Abstract (43 Views)
Aims: Flood are one of the natural hazards that cause financial and human losses every year. This research was carried out to determine the flood risk zones in the Friesian watershed with ANNS artificial neural network model and SVM support vector machine to identify the influential factors in the occurrence of floods in the region, necessary measures for control, planning and protective and management measures in To reduce the risk of flooding.
Methodology: 14 environmental parameters were used for flood risk zoning. Then, 150 flood points in the basin were extracted using satellite images and randomly divided into 70% for model training and 30% for validation. Then, the layers were weighted, and the flood potential maps were classified into 5 categories: very high, high, medium, low, and very low, using the natural fracture method. Then, the accuracy of the models used in the research was investigated using the ROC performance characteristic curve method.
Findings: The accuracy of the models used in the research was checked using the ROC performance characteristic curve method. According to the obtained results, the value of AUC in the ANNS model was 0.923, and in the SVM model, 0.898, which indicates the better performance of the ANNS model.
Conclusion: The zoning results showed that the basin's flooding occurred mainly in low slopes, resistant and impermeable formations, lower elevations, and lands adjacent to rivers (floodplains). High and very high-risk areas are located in the central and outlet parts of the basin. Due to the flood-prone area, management and watershed management measures are necessary.
Methodology: 14 environmental parameters were used for flood risk zoning. Then, 150 flood points in the basin were extracted using satellite images and randomly divided into 70% for model training and 30% for validation. Then, the layers were weighted, and the flood potential maps were classified into 5 categories: very high, high, medium, low, and very low, using the natural fracture method. Then, the accuracy of the models used in the research was investigated using the ROC performance characteristic curve method.
Findings: The accuracy of the models used in the research was checked using the ROC performance characteristic curve method. According to the obtained results, the value of AUC in the ANNS model was 0.923, and in the SVM model, 0.898, which indicates the better performance of the ANNS model.
Conclusion: The zoning results showed that the basin's flooding occurred mainly in low slopes, resistant and impermeable formations, lower elevations, and lands adjacent to rivers (floodplains). High and very high-risk areas are located in the central and outlet parts of the basin. Due to the flood-prone area, management and watershed management measures are necessary.
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