Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms.

Flooding is a devastating natural disaster that causes fatalities and property damage worldwide. Effective flood susceptibility mapping (FSM) has become crucial for mitigating flood risks, especially in urban areas. This study evaluates the performance of artificial neural network (ANN) algorithms for FSM using machine learning classification. Traditional flood prediction models face limitations due to data complexity and computational constraints. This research incorporates artificial intelligence, particularly evolutionary algorithms, to create more adaptable and robust flood prediction models. Four specific algorithms-black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)-were tested for predicting flood occurrences in the Fars region of Iran. These evolutionary algorithms simulate natural processes like selection, mutation, and crossover to optimize flood predictions and management strategies, improving adaptability in dynamic environments. The novelty of this study lies in using evolutionary AI algorithms to not only predict floods more accurately but also optimize flood management strategies. The ANN was trained with geographical data on eight flood-impacting factors, including elevation, rainfall, slope, NDVI, aspect, geology, land use, and river data. The models were validated with historical flood damage data from the Fars area using metrics like mean square error (MSE), mean absolute error (MAE), and the receiver operating characteristic (ROC) curve. Results showed significant improvements in accuracy for BHA-MLP, FSA-MLP, MVO-MLP, and HBO-MLP, with accuracy indices and AUC values increasing. The study concludes that hybridized models offer an effective and economically viable approach for urban flood vulnerability mapping, providing valuable insights for flood preparedness and emergency response strategies.