Spatial Flood Forecasting Modeling under Lack of Data Using RS and Optimized Support Vector Machine: A Case Study of the Zahedan Watershed

This research was conducted with the aim of producing flood susceptible area maps of the Zahedan catchment in Iran during lack of data using remote sensing. To determine the factors to be included in the model, factors identified in previous studies were prepared using remote sensing and were evaluated by the information gain ratio (IGR) method and the multicollinearity diagnostic test, and ultimately 10 factors with the highest IGR values were chosen as the most effective factors in the region. The flood inventory map was produced by processing the Sentinel-1 satellite data. The gathered data were used to map flood susceptibility maps with the SVM model optimized with IWO and ACO. The prediction accuracy of the models was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the area under the receiver operating characteristic (ROC) curve (AUC). While both optimization algorithms are effective in improving the performance of SVM, the hybrid of SVM with IWO shows the best performance in terms of statistical measures and Friedman test results. The results of the study confirm the good performance of the proposed models in spatial prediction of flood susceptible areas. Since over half of the urban lands of the city of Zahedan are at moderate to very high risk of flooding, this area needs more attention in terms of flood prevention and control measures.

Prevention of flood events will not always be practical, so using flood mitigation methods is considered the best strategy for flood risk management. The purpose of using these methods is to significantly reduce flood damage by evaluating flood-prone areas and taking protective measures in areas. In this study, data mining methods were used to select parameters affecting floods in the region. One of the problems of the study area is the lack or absence of information. Considering that there was no complete information about the parameters required in this research in the studied area, information was obtained using remote sensing and then optimization was done using the data obtained by the support vector machine model. The flood sensitivity map of the area was drawn. The flood sensitivity map will help to identify high-risk and low-risk areas for flood prevention planning. The results showed that 9% of the studied area is in very high susceptibility, 3% in high susceptibility, 3% in moderate susceptibility, 7% in low susceptibility and 78% in very low susceptibility. Also, in the meantime, more than half of Zahedan’s urban area is located in the medium to very high sensitivity area, which should be given special attention.