چکیده مقاله
In regions like Iran, where the climate is highly variable and precipitation plays a critical role in water resource management and agriculture, reliable short term rainfall forecasting is essential This study focuses on 24 hourly precipitation prediction one day lag at the Mashhad meteorological station using daily data from 2000 to 2023 Several ensemble based machine learning algorithms were evaluated, including Random Forest, AdaBoost, CatBoost, LightGBM, and XGBoost regressors Ten key one day lagged meteorological variables, including precipitation, temperature, humidity, wind speed, sunshine hours, and sea level pressure, were used as input features The results showed that the XGBoost regression model achieved the best balance between predictive accuracy and generalization, with a test MAE of 0 84 mm, RMSE of 2 31 mm, and R² of 0 174 While the model demonstrated strong capability in distinguishing dry and wet days, its performance in capturing high intensity rainfall events remained limited This study highlights the potential of ensemble machine learning methods for data driven rainfall forecasting in semi arid climates It also emphasizes the need for future improvements through deep learning architectures e g , GRU, TCN, Transformers and integration with upper atmospheric features or numerical weather prediction NWP systems for enhanced modeling of extreme rainfall events
کلیدواژهها
نویسندگان
Amirhossein Babaeian
Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Mahdi Rostamzadeh
Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Parisa Hormozzadeh
Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Alireza Shadman
Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
شیوه ارجاع
Babaeian, Amirhossein and Rostamzadeh, Mahdi and Hormozzadeh, Parisa and Shadman, Alireza,1404,Machine Learning–Driven Rainfall Prediction: A Case Study at Mashhad Weather Station, Iran,11th National Conference on Interdisciplinary Research in Engineering and Management,Tehran
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مجموعه مقالات یازدهمین همایش ملی تحقیقات میان رشته ای در علوم مهندسی و مدیریت31 تیر 1404 · تهران