Investigating the Performance of Machine Learning Models in Estimating Monthly Dam Evaporation: A Case Study of Sidi Mhamed Ben Aouda Dam, Wadi Mina Basin, Algeria

Achite M., Emami S., Emami H., KATİPOĞLU O. M., Pandey K., Hajimirzajan A., ...More

Pure and Applied Geophysics, 2025 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2025
  • Doi Number: 10.1007/s00024-025-03737-8
  • Journal Name: Pure and Applied Geophysics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Geobase, INSPEC
  • Keywords: climate change, evaporation, machine learning models, Water resource management
  • Erzincan Binali Yildirim University Affiliated: Yes

Abstract

Effective water resource management in arid and semi-arid regions requires accurate estimation of hydrological parameters such as evaporation. This study investigates the monthly evaporation of the Sidi-M’Hamed Ben Aouda dam basin in northwest Algeria using six machine learning models: random forest (RF), extra tree (ET), gradient boosting (GB), category boosting (CatBoost), light gradient boosting machine (LGBM), and multi-layer perceptron (MLP). Climatic inputs (temperature, relative humidity, wind speed, and sunshine hours) from 1978 to 2023 were used to train and test the models. The findings reveal that the ET model achieved the best balance between accuracy and computational speed, while the RF model provided the highest overall accuracy. GB had a faster runtime with slightly reduced accuracy, whereas CatBoost and MLP underperformed. This comparative analysis highlights the suitability of ensemble tree-based models, particularly RF and ET, for accurate and efficient evaporation prediction, supporting water resource planning in data-scarce and climate-sensitive regions.