Using Artificial Neural Network (ANN) to analyze land vulnerability to flood risk.

Case study: Irbid city

Abstract

This study aimed to prepare flood risk maps in the city of Irbid for the year 2022 and identify the places most vulnerable to flooding. Flood risk modeling using artificial neural network for future forecasting. The study used the descriptive analytical method. To identify and analyze the factors influencing the occurrence of floods during the study period, using the composite weights method and the Arc Gis geographic information system program, and using the Statistical Package for Social Sciences (SPSS) program to build an artificial neural network model and access a map of future flood risks. The study concluded that, through artificial neural network analysis, the city of Irbid will witness a very high risk of flooding in the city center in the future, with an area of 1.2 km2 and a rate of (3.4%), while the areas with high risk will expand to include northern, northeastern, northwestern, and southern areas. It is largely southeastern, with an area of 27.3 km2 and a rate of (74.4%), while the southwestern and northwestern regions represent a medium risk and its development at the expense of low risk, with an area of 8.2 km2 and a rate of (22.3%), while the low risk areas in the northwest of the city are shrinking. Significantly. The study concluded that the artificial neural network (ANN) has high predictive accuracy for flood risks.