Leveraging Heterogeneous Data in Flood Monitoring and Forecasting Using Machine Learning
Alzhanov A. Rakhymbek K. Nugumanova A.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE European Technology and Engineering Management Summit, E-TEMS
2025Issue 2025246 - 251 pp.
Floods are among the most destructive natural hazards, and accurate forecasting is essential for effective risk management and response. However, in many regions, sparse in-situ monitoring networks with a limited number of measurable parameters may compromise forecasting accuracy. This study focuses on the Uba River Basin in the East Kazakhstan region and investigates whether integrating ERA5-Land reanalysis data with observed hydrometeorological measurements improves hydrological forecasting, additionally providing an analysis of shared variables. Models trained solely on observed data are compared with those incorporating ERA5-Land features. Leave-year-out cross-validation across six test years reveals that while incorporating ERA5-Land generally enhances forecast accuracy, the improvement is minimal or non-existent in specific years, suggesting that dominant local hydrological fluctuations may already be well-captured by in-situ data alone. Bias analysis further indicates that ERA5-Land tends to underestimate extreme precipitation events, potentially affecting flood peak predictions. Overall, despite its limitations, ERA5-Land proves to be a valuable supplementary data source for improving flood forecasting and the findings provide insights into optimizing data selection strategies to further refine flood forecasting in data-sparse regions.
discharge , ERA5-Land , flood forecasting , LSTM , water level
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Astana IT University, Science and Innovation Center of Big Data and Blockchain Technologies, Astana, Kazakhstan
Sarsen Amanzholov East Kazakhstan University, Laboratory of Digital Technologies and Modeling, Oskemen, Kazakhstan
Astana IT University
Sarsen Amanzholov East Kazakhstan University
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