Comparative Evaluation of Machine Learning Algorithms for Evaporation Estimation in Shahrood Region

Accurate prediction of evaporation is critical for effective water resource management, particularly in arid and semi-arid regions. This research evaluates the performance of five machine learning algorithms Decision Tree, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Artificial Neural Network in estimating monthly evaporation rates using meteorological data collected at Shahrood Synoptic Station from ۱۹۹۲ to April ۲۰۲۵. The dataset includes key climatic parameters such as average temperature, wind speed, precipitation, and relative humidity. Model performance was assessed through four metrics: Mean Absolute Error, Coefficient of Determination, Kling-Gupta Efficiency, and Average Absolute Relative Deviation. Results indicate that the Random Forest model outperformed all others, achieving the lowest MAE of ۱۹.۹۴ mm, highest KGE of ۰.۹۷۳, and lowest AARD of ۰.۵۲۱, reflecting superior accuracy and stability. The Artificial Neural Network model also demonstrated strong predictive capability, closely followed by Support Vector Regression, while simpler models like Decision Tree and K-Nearest Neighbors showed comparatively weaker performance due to their limited ability to capture complex evaporation dynamics. Temporal analysis revealed that all models effectively captured seasonal evaporation patterns, with Random Forest and Artificial Neural Network most accurately tracing peak and trough fluctuations. The results demonstrate that machine learning models possess strong predictive accuracy for evaporation estimation and offer a reliable approach for assessing evaporation and water loss.

کلیدواژه ها:

Evaporation estimation ، Machine learning algorithms ، Artificial Neural Network ، Shahrood ، Meteorological Data