Author
Jannatul Ferdous Ruma
Journal
Results in Engineering
Paper Keyword
Long short-term memory,Multivariate time series,Neural network,Particle swarm optimization,Water level forecast
Abstract
Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demon strated that more advanced and sequenced-based deep learning techniques, like long short-term memory (LSTM) networks, are superior at forecasting hydrological data. However, historically, most LSTM hyperparameters were based on experience, which typically did not produce the best outcomes. The Particle Swarm Optimization (PSO) method was utilized to adjust the LSTM hyperparameter to increase the capacity to learn data sequence char acteristics. Utilizing water level observation data from stations along Bangladesh’s Brahmaputra, Ganges, and Meghna rivers, the model was utilized to estimate flood dynamics. The Nash Sutcliffe efficiency (NSE) coeffi cient, root mean square error (RMSE), and MAE were used to assess the model’s performance, where PSO-LSTM model outperforms the ANN, PSO-ANN, and LSTM models in predicting water levels in all stations. The PSO LSTM model provides improved prediction accuracy and stability and improves water level forecasting accu racy at varying lead times. The findings may aid in sustainable flood risk mitigation in the study region in the future.
