Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning

Mukhamediev R.I. Terekhov A. Sagatdinova G. Amirgaliyev Y. Gopejenko V. Abayev N. Kuchin Y. Popova Y. Symagulov A.
December 2023 Multidisciplinary Digital Publishing Institute (MDPI)

Remote Sensing
2023 #15 Issue 23

Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring.

ensemble machine learning , machine learning , remote sensing data , Sentinel-2 , the Ili River , water level estimation

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Institute of Automation and Information Technology, Satbayev University (KazNRTU), 22 Satbayev Street, Almaty, 050013, Kazakhstan
Institute of Information and Computational Technologies, Pushkin Str., 125, Almaty, 050010, Kazakhstan
International Radio Astronomy Centre, Ventspils University of Applied Sciences, Ventspils, LV-3601, Latvia
Department of Natural Science and Computer Technologies, ISMA University of Applied Sciences, Riga, LV-1019, Latvia
RSE Kazhydromet, 11/1 Mangilik El avenue, Astana, 010000, Kazakhstan
Transport and Telecommunication Institute, Riga, LV-1019, Latvia

Institute of Automation and Information Technology
Institute of Information and Computational Technologies
International Radio Astronomy Centre
Department of Natural Science and Computer Technologies
RSE Kazhydromet
Transport and Telecommunication Institute

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