A Hybrid Deep Learning Method for Forecasting Reservoir Water Level from Sentinel-2 Satellite Images

Global climate change, along with the rapid increase of the population, has put significant pressure on water security. A water reservoir is an effective solution for adjusting and ensuring water supply. In particular, the reservoir water level is an essential physical indicator for the reservoirs. Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies. In recent years, deep learning models have been widely applied to solve forecasting problems. In this study, we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9, ConvLSTM, and linear interpolation to predict reservoir water levels. It utilizes data from Sentinel-2 satellite images, generated from visible spectrum bands (Red-Blue-Green) to reconstruct true-color reservoir images. Adam is used as the optimization algorithm with the loss function being MSE (Mean Squared Error) to evaluate the model’s error during training. We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam. To assess its performance, we also conducted comparative experiments with other related models, including SegNet_ConvLSTM and UNet_ConvLSTM, on the same dataset. The model performances were validated using k-fold cross-validation and ANOVA analysis. The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models. It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.