Prediction of irrigation event occurrence at farm level using optimal decision trees

Irrigation water demand is highly variable and depends on farmers' decision about when to irrigate. Their decision affects the performance of the irrigation networks. An accurate daily prediction of irrigation events occurrence at farm scale is a key factor to improve the management of the irrigation districts and consequently the sustainability of the irrigated agriculture. In this work, a hybrid heuristic methodology that combines Decision Trees and Genetic Algorithm has been developed to find the optimal decision tree to model farmer's behaviour, predicting the occurrence of irrigation events. The methodology has been tested in a real irrigation district and results showed that the optimal models developed have been able to predict between 68% and 100% of the positive irrigation events and between 93% and 100% of the negative irrigation events.