Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 157, Issue -, Pages 173-180Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2018.12.043
Keywords
Artificial intelligence; Multiobjective genetic algorithm; Irrigation scheduling; Expert systems
Funding
- FPU grant (Formacian de Profesorado Universitario) from the Spanish Ministry of Education, Culture and Sports
- Spanish Ministry of Economy and Competitiveness [AGL2014-59747-C2-2-R]
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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.
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