Forecasting of applied irrigation depths at farm level for energy tariff periods using Coactive neuro-genetic fuzzy system

Abstract

Abstract Nowadays, water scarcity and the increase in energy demand and their associated costs in pressurized irrigation systems are causing serious challenges. In addition, most of these pressurized irrigation systems has been designed to be operated on-demand where irrigation water is continuously available to farmers complexing the daily decision-making process of the water user association’ managers. Know in advance how much water will be applied by each farmer and its distribution during the day would facilitate the management of the system and would help to optimize the water use and energy costs. In this work, a new hybrid methodology (CANGENFIS) combining Multiple input -Multiple output, fuzzy logic, artificial neural networks and multiobjective genetic algorithms was developed to model farmer behaviour and short-term forecasting the distribution by tariff period of the irrigation depth applied at farm level. CANGENFIS which was developed in Matlab was applied to a real water user association located in Southwest Spain. Three optimal models for the main crops in the water user association were obtained. The average for all tariff periods of the representability (R2) and accuracy of the forecasts (standard error prediction, SEP) were 0.70, 0.76% and 0.85% and 19.9%, 22.9% and 19.5%, for rice, maize and tomato crops models, respectively.