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Optimisation of water demand forecasting by artificial intelligence with short data sets
- González Perea, Rafael, Camacho Poyato, Emilio, Montesinos, Pilar, Rodríguez Díaz, Juan Antonio
- Biosystems engineering 2019 v.177 pp. 59-66
- Bayesian theory, algorithms, artificial intelligence, communications technology, data collection, decision making, energy, freshwater, irrigated farming, irrigation scheduling, irrigation water, managers, neural networks, prediction, Spain
- Irrigated agriculture is one of the key factors responsible for decreasing freshwater availability in recent years. Thus, the development of new tools which will help Irrigation District managers in their daily decision making process about the use of water and energy is essential. On the other hand, the new era of Big Data and information and communications technologies (ICT) has made it possible to have a larger amount of information available, leading to the development of new prediction tools. However, the quality and quantity of this information in many fields such as irrigated agriculture is limited. Consequently, the way in which the development of new predictive models is addressed must be reformulated. Thus, in this work, a new methodology to provide short-term forecasting of daily irrigation water demand when data availability is limited has been developed by coupling dynamic Artificial Neural Networks (ANN) architecture, the Bayesian framework and Genetic Algorithms (GA). The methodology was applied in the Bembézar MD Irrigation District (Southern Spain). The developed model improved the prediction accuracy by between 3% and 11% with respect to previous work. The best ANN model had a Standard Error Prediction (SEP) and a determination coefficient (R2) of 8.7% and 96%, respectively. The accuracy of the model developed makes it a powerful tool for the daily management of irrigation districts.