با همکاری انجمن هیدرولیک ایران
نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، ایران
2 گروه علوم و مهندسی آب، دانشگاه بیرجند، بیرجند، ایران
3 دانشجوی کارشناسی ارشد، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند.
کلیدواژهها
عنوان مقاله English
نویسندگان English
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.
کلیدواژهها English
