Attentional ensemble model for accurate discharge and water level prediction with training data enhancement

Discharge is water flow from a higher to a lower elevation resulting from precipitation or surface runoff. It is an essential resource for human society, including drinking water and irrigation. Forecasting discharge and water level are crucial in establishing plans to secure water sources and predict floods. Deep learning has recently emerged as a potential solution for forecasting discharge and water levels. However, it faces three challenges: training data shortage, noise existence, and underestimation tendency. This research offers a novel deep learning-based method addressing the abovementioned shortcomings. To overcome data scarcity and improve prediction accuracy, we leverage the strengths of the one-dimensional Convolutional Neural Network (1D-CNN), Long short-term memory (LSTM), and Ensemble learning technique. Specifically, 1D-CNN uses a kernel moving in one direction and executing the convolution operations to extract the correlations between the features. LSTM helps to capture temporal relationships inside the data, while ensemble learning exploits multiple learning models for better predictive performance. Besides, Singular-Spectrum Analysis (SSA) technique is applied to decompose the input data into several components and eliminate noise-like ones while retaining essential parts. Besides, the attention mechanism is leveraged to assign higher weights for more essential features, further enhancing the accuracy. Finally, we utilize the Linear Exponential (LINEX) loss to prevent under- and overestimation. The experiments show that our proposal outperforms existing approaches regarding all metrics. Notably, our method improves the Nash–Sutcliffe model Efficiency coefficient (NSE) (the most critical metric) by up to 40% and 34% concerning one-step-ahead and multistep-ahead, respectively, compared to existing approaches.