Open Access

ARTICLE

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

Hoang Thi Minh Chau^1,2,3, Tran Thi Ngan^4,*, Nguyen Long Giang⁵, Tran Manh Tuan⁶, Tran Kim Chau⁷

1 Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, 100000, Vietnam
2 Faculty of Information Technology, University of Economics Technology for Industries, Hanoi, 100000, Vietnam
3 Artificial Intelligence Research Center, VNU Information Technology Institute, Vietnam National University, Hanoi, 100000, Vietnam
4 International School, Vietnam National University, Hanoi, 100000, Vietnam
5 Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, 100000, Vietnam
6 Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, 100000, Vietnam
7 Faculty of Water Resources Engineering, Thuyloi University, Hanoi, 100000, Vietnam

* Corresponding Author: Tran Thi Ngan. Email:

(This article belongs to the Special Issue: Transforming Image Enhancement with Efficient AI and Large Language Models)

Computers, Materials & Continua 2025, 83(3), 4915-4937. https://doi.org/10.32604/cmc.2025.062784

Received 27 December 2024; Accepted 23 April 2025; Issue published 19 May 2025

Abstract

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.

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