Predictive modeling of Pan Evaporation using Random Forest Algorithm along with Features Selection

Random Forest is a learning method that can be used for classification and regression problems; it operates by constructing decision trees at training time and output the predicted results. In this study, the algorithm is used to predict the Pan Evaporation for Karnal district, India. Random forest is also adopted to select the important features which highly influence the evaporation conditions. The weather of four lag weeks from the week of forecast is used to form indices that are considered for the model development. The algorithm is trained using thirty-one-year data (1973-2003) and subsequent year (2004-05) which is not utilized for model development is used as a testing set. The developed random forest model is further compared with the artificial neural network with backpropagation algorithm. The performance of the models is measured using mean square error, which shows that the predicted values are in close approximation with the observed one but the random forest model has better predictions than the artificial neural network.