Spatial rainfall estimation applying machine learning techniques in a semi-arid basin

ABSTRACT Rainfall is one of the most important meteorological phenomena since it provides water to the Earth's surface, which has a significant impact on the daily life of human beings. Gaining the knowledge of its behavior in a semi-arid basin is an important and challenging task to take advantage of this natural resource, given that water is usually scarce in such regions. Artificial intelligence and machine learning algorithms help to identify rainfall patterns and trends within a region. Multiple linear regression, random forest (RF), support vector machine, and artificial neural network (ANN) algorithms were implemented using daily rainfall data from climatological stations located within the basin using one station as a predictor variable and the remaining ones as input variables. The metrics to evaluate the model were the coefficient of determination (R2), mean absolute error, root mean square error, and the Kling–Gupta efficiency coefficient. The results showed that the daily rainfall prediction is better individually than overall, finding that the models obtained by RF and ANN simulate better daily rainfall in the basin.