Data-Driven Flood Forecasting Using ANN: A Resource-Efficient Approach for High-Risk Regions

Flood forecasting is essential for hydrological assessment and catastrophe mitigation, particularly in flood-prone areas such as Bangladesh. Nonetheless, the direct measurement of water levels (WL) and discharge frequently encounters obstacles related to time, technological limits, and economical constraints. This study posits that flood levels can be accurately predicted utilising accessible data during flood events, employing a trained Artificial Neural Network (ANN) model. The complexity of hydrological systems, exacerbated by transboundary contributions from significant rivers like the Brahmaputra-Jamuna, hinders accurate forecasting. To tackle these problems, the study employed Artificial Neural Networks (ANN), a flexible and data-driven methodology adept at modelling non-linear relationships, to predict flood water levels with a lead time of up to seven days in Sirajganj, a district particularly susceptible to river flooding and bank erosion. Daily Data on water levels and rainfall were collected from the Bangladesh Water Development Board (2002–2015) for the monsoon season (May–October) were analysed, utilising information from four rainfall stations and six water level stations located 62–237 km upstream. The ANN model, employing a Sigmoid activation function with one to three hidden layers, indicated that augmenting the number of hidden layers provided only negligible enhancements in performance. Performance metrics, such as the goodness-of-fit (R²: 0.985–0.554), Root Mean Square Error (RMSE: 0.024–0.617), and Mean Absolute Error (MAE: 0.087–0.604), demonstrated a marginal improvement when rainfall and water level data were combined. This study highlights the efficacy of Artificial Neural Networks (ANN) in tackling hydrological prediction issues, confirming its ability to utilise readily accessible datasets to provide reliable and effective flood forecasts, thus aiding disaster preparedness and mitigation efforts in resource-limited areas such as Bangladesh.