Simulation of Qanat Discharge in Balade Ferdows Using Machine Learning Algorithms under Drought Conditions

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 ۱۰-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 = ۰.۹۳, R² = ۰.۹۲ with the lowest RMSE = ۱۲.۲۱. 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.

کلیدواژه ها:

Qanat ، machine learning ، Ensemble Learning ، Drought ، groundwater resources