DEVELOPMENT OF THE INTEGRATED WATER RESOURCES MONITORING AND FORECASTING MODULE FOR DECISION SUPPORT SYSTEMS AT HYDROTECHNICAL STRUCTURES
DOI:
https://doi.org/10.37943/22NEJN3212Abstract
Nowadays, it is necessary to use monitoring and forecasting technologies for effective water resources management at water management facilities. The objective of this study is to develop and verify an integrated approach to water resources forecasting with the task of identifying features for forecasting, designing a data preprocessing submodule and a forecasting module. The workflow diagram of the water forecasting system includes sequential stages of data collection, preprocessing, filtering, feature extraction, and training. Sentinel-2 and MODIS satellite sources were used for data preprocessing. Predictors for the formation of time series by normalized difference water index (NDWI) and water surface temperature (LST) were selected in the feature engineering stage. The XGBoost Regressor algorithm was chosen due to its ability to model nonlinear relationships and feature interactions. Excluding winter months improved the model performance for all metrics, which demonstrates the importance of seasonal filtering when working with optical satellite data. The machine learning algorithm takes into account the analysis of satellite data (NDWI and LST indices) through the Google Earth Engine (GEE) platform. Both seasonal and long-term dynamics of water volumes in the Tasotkel reservoir are monitored for the period from 2020 to 2024. In practice, image initial filtering submodules were developed using linear regression and the XGBoost model. Model trained without winter data shows high performance using Metrics Mean Absolute Error (MAE) of 52.793, Root Mean Squared Error (RMSE) of 60.276, coefficient of determination (R2) of 0.673 and Mean Squared Error (MSE) of 3633.252 metrics. However, a decrease in clarity was observed due to snow and ice on reflective properties in winter. For the purpose of rational water resources management, the combination of satellite images and machine learning algorithms in this study shows the prospects for development.
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