Reconstructing Missing Streamflow Data in Mountainous, Snow-Dominated Regions Using Optimized Hybrid Machine Learning

Abstract Accurate streamflow estimation is pivotal for hydrological research and water resource management, particularly in mountainous, snow-dominated regions where missing data can significantly impair watershed modeling and water budget evaluations. This study presents an advanced hybrid machine learning framework to impute missing streamflow records at a strategically located observation station within a snow-abundant sub-basin of the Euphrates-Karasu Basin in Erzurum Province, Eastern Turkey. Leveraging daily meteorological and hydrological data collected over four decades (1983–2023), we developed and rigorously evaluated several predictive models using comprehensive performance metrics and rank-based analysis. In addition to conventional approaches-including a single Artificial Neural Network (ANN), CatBoost, and Deep Neural Network (DNN)-the study also compares innovative hybrid models in which ANN parameters were optimized using the Whale Optimization Algorithm (WOA) and the Hydrologic Cycle Algorithm (HCA). Notably, the hybrid models demonstrated superior predictive accuracy over traditional methods, thereby underscoring their potential to bridge data gaps in challenging terrains. The results affirm the efficacy of the proposed framework and its promise as a robust methodological foundation for future investigations in watershed modeling, drought assessment, and water budget analysis.