Evaluating machine learning models for estimating evapotranspiration in Colombia's Cauca River Valley.

Water loss in soil and plants occurs through processes such as evaporation and transpiration, giving rise to evapotranspiration. In water resource management and precision agriculture, it is important to have accurate estimates of water loss in order to optimize agricultural irrigation and improve crop productivity. To estimate evapotranspiration data, this study used different machine learning methods such as multiple linear regression, polynomial regression, and neural networks using libraries such as Scikit-learn, Statsmodels, TensorFlow, and Keras. Climate data gathered by the Cenicaña weather station located in Florida, department of Valle del Cauca, Colombia, were used to train the machine learning models and later compare evapotranspiration estimates based on the FAO 56 Penman-Monteith method. To ensure the best estimate, 13 different data combinations were used, each combination involving three of the six parameters studied. Combinations involving relative humidity and minimum, maximum or mean temperatures showed the lowest precision, presenting values such as R = 0.7, R