Application of Machine Learning in Sustainable Qanat Management: Qanat Discharge Estimation Using the Random Forest Algorithm and Sensitivity Analysis (SHAP and Permutation Importance) in Qaen Plain

Zeraati Neyshabouri, Sepide; Fouladi Nasrabad, Mohamad; Javaheri, Mahna

doi:10.22077/jaaq.2026.10686.1135

Application of Machine Learning in Sustainable Qanat Management: Qanat Discharge Estimation Using the Random Forest Algorithm and Sensitivity Analysis (SHAP and Permutation Importance) in Qaen Plain

Document Type : Original Article

Authors

Sepide Zeraati Neyshabouri

Mohamad Fouladi Nasrabad

mahna javaheri

Department of Water Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.

https://doi.org/10.22077/jaaq.2026.10686.1135

Abstract

Arid and semi-arid regions of Iran, particularly the Qaen Plain in South Khorasan Province, face severe water resource limitations. Qanats, as traditional sustainable water supply systems with over 3,000 years of history, play a vital role in local agriculture and livelihoods. However, accurately estimating their discharge is challenging due to climate change impacts and data limitations. This study employs the Random Forest (RF) algorithm—a powerful machine learning method—to estimate qanat discharges in the Qaen Plain using 12 years of data (2007–2018 CE). Input variables included historical observed discharges, mean temperature, monthly precipitation, and elevation of each qanat. After preprocessing (normalization and missing data imputation), the dataset was split into training (70%) and testing (30%) sets. The model was trained with optimized hyperparameters (max_depth=10, max_features=0.5, min_samples_split=5, n_estimators=500). Results showed strong performance: RMSE = 4.27 L/s, NSE = 0.54, r = 0.76, KGE = 0.51, and NRMSE = 15.19%. Sensitivity analysis using SHAP and Permutation Importance identified elevation as the key variable (mean |SHAP| = 2.16), while precipitation exhibited a slight negative influence (SHAP = -0.013). These findings validate RF's efficacy and provide a practical framework for water resource management in arid regions.

Keywords

Qanat discharge prediction

Climatic data

Spatiotemporal variations

Sensitivity analysis

Subjects

ًQanat and aquifer

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