Flood Prediction and Forecasting Using Anns and Fuzzy Logic Model in Sylhet, Bangladesh

Floods are a significant threat to the environment, human life, and infrastructure in Sylhet, Bangladesh. The region's unique geography and climate make it prone to frequent and severe floods, which can cause significant economic and social losses. To address this issue, this study proposes an intelligent flood management system that combines the power of artificial neural networks (ANNs) for rainfall prediction and fuzzy logic for flood forecasting. The system is designed to provide accurate and real-time predictions of rainfall and flood events, enabling effective decision-making and mitigation strategies. The ANNs are trained using historical rainfall data to predict future rainfall patterns, whereas the fuzzy logic component uses the predicted rainfall data to forecast flood events. The system was tested on real-world data from Sylhet and demonstrated high accuracy in predicting rainfall and flood events. The ANN model was developed via a feed-forward backprop network with three input variables and one output variable (rainfall). A TRAINLM training function, LEARNGDM adaptation learning function, and MSE performance function were used. The ANN architecture consisted of two hidden layers with eight neurons each, with LOGSIG and PURELIN transfer functions for the first and second layers, respectively. The fuzzy logic component employs a Mamdani-type fuzzy inference system (FIS) with twelve rules, using rainfall and river level as inputs and flooding as the output. Triangular (TRIMF) and trapezoidal (TRAPMF) membership functions were utilized. The results of the ANN model revealed a mean square error (MSE) with a suitable curve and a correlation coefficient (R) over 0.9, indicating a strong correlation between the predicted and actual values. Additionally, we obtain an outstanding mean absolute error (MAE) value. The hybrid approach combining ANNs and fuzzy logic demonstrated high accuracy in flood forecasting, outperforming traditional methods. The proposed system can provide early warning of flood events, enabling timely mitigation measures and reducing the impact on communities.