University of Birjand-Research Group of Drought and Climate Change Journal of Drought and Climate change Research 3092-6076 Articles in Press 2026 05 29 Simulation of Qanat Discharge in Balade Ferdows Using Machine Learning Algorithms under Drought Conditions Simulation of Qanat Discharge in Balade Ferdows Using Machine Learning Algorithms under Drought Conditions 3994 10.22077/jdcr.2026.10872.1209 FA Mostafa Yaghoobzadeh Water engineering department, university of birjand- birjand- Iran 0000-0002-1041-1941 Mehdi Amirabadizadeh Water engineering department, university of birjand 0000-0002-8626-4036 Amir Khayat PhD student of water engineering department, university of Birjand 0009-0003-3450-8371 Zahra Akhondi Iran- Birjand 0009-0003-1368-7809 Journal Article 2026 01 12 Qanats, as one of the most important indigenous water supply systems in arid and semi-arid regions, have been severely affected by climate change and the ongoing recent droughts. In this regard, employing advanced machine learning models can play a key role in developing reliable forecasting systems to support climate change adaptation planning. objective of this research is to simulate the monthly discharge of the Balade Qanat complex in Ferdows County using a set of machine learning models, including single algorithms such as XG Boost, SVR, Random Forest, and Gradient Boosting, as well as an advanced ensemble approach, Stacking. This simulation uses climatic, hydrological data, and drought indices over a 10-year period. dominant approach in modeling is comparing the performance of individual models against the final ensemble model. The obtained results showed that under the region’s variable climatic conditions, the Stacking ensemble approach exhibited a significantly stronger performance than single models like XG Boost. Stacking model was selected as the optimal model, achieving the highest coefficient of determination (R²) and the highest (KGE = 0.93, R² = 0.92 with the lowest RMSE = 12.21. This superior performance emphasizes the capability of Stacking models in reducing variance and correcting systematic biases of individual models when dealing with the complex and nonlinear behavior of qanat discharges. It is concluded that the Stacking model, due to its ability to extract complex nonlinear patterns and improve generalization, is a superior management tool for decision-making in the sustainable exploitation of qanat water resources in water-stressed climates. Qanats, as one of the most important indigenous water supply systems in arid and semi-arid regions, have been severely affected by climate change and the ongoing recent droughts. In this regard, employing advanced machine learning models can play a key role in developing reliable forecasting systems to support climate change adaptation planning. objective of this research is to simulate the monthly discharge of the Balade Qanat complex in Ferdows County using a set of machine learning models, including single algorithms such as XG Boost, SVR, Random Forest, and Gradient Boosting, as well as an advanced ensemble approach, Stacking. This simulation uses climatic, hydrological data, and drought indices over a 10-year period. dominant approach in modeling is comparing the performance of individual models against the final ensemble model. The obtained results showed that under the region’s variable climatic conditions, the Stacking ensemble approach exhibited a significantly stronger performance than single models like XG Boost. Stacking model was selected as the optimal model, achieving the highest coefficient of determination (R²) and the highest (KGE = 0.93, R² = 0.92 with the lowest RMSE = 12.21. This superior performance emphasizes the capability of Stacking models in reducing variance and correcting systematic biases of individual models when dealing with the complex and nonlinear behavior of qanat discharges. It is concluded that the Stacking model, due to its ability to extract complex nonlinear patterns and improve generalization, is a superior management tool for decision-making in the sustainable exploitation of qanat water resources in water-stressed climates. https://jdcr.birjand.ac.ir/article_3994_adf8d7f8c53c8688e63a02bfb3055497.pdf