Environmental Factors and Machine Learning Prediction of Water in semnan Evaporation Rate

کد مقاله : 1088-ICOC

نویسندگان

مریم رضایی * قراردادی

چکیده مقاله

1. Introduction Water evaporation is one of the most important processes in the hydrological cycle and plays a significant role in water resource management, agriculture, and climate modeling. Reference evapotranspiration (ET₀) is one of the key components of the hydrological cycle and irrigation management [1]. The aim of this study is to predict the amount of water evaporation under the environmental conditions of Semnan city using meteorological data and machine learning algorithms. 2. Theoretical Section Environmental variables from the NASA POWER system were used, and reference evapotranspiration (ET₀) was calculated using the FAO-56 equation [2,3]. Random Forest, K-Nearest Neighbors, and Linear Regression models were applied to predict ET₀ and evaluated using standard metrics and R². 3. Results The results indicate that the Random Forest model achieved the highest prediction accuracy compared to K-Nearest Neighbors and Linear Regression. The detailed performance metrics of the models are presented in Table 1 4. Conclusion The results show that using NASA POWER data and machine learning models, especially Random Forest, provides an accurate approach for estimating reference evapotranspiration in the climatic conditions of Semnan.

کلیدواژه ها

Evaporation, Machine Learning, Random Forest, Semnan

وضعیت: پذیرفته شده