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.

如今，水资源短缺和能源需求的增加及其在加压灌溉系统中的相关成本正在带来严峻的挑战。此外，这些加压灌溉系统中的大多数都设计为按需运行，农民可以持续获得灌溉用水，从而使用水协会管理人员的日常决策过程变得复杂。提前了解每个农民将使用多少水及其在白天的分配将有助于系统管理，并有助于优化用水和能源成本。在这项工作中，一种新的混合方法（CANGENFIS）结合了多输入-多输出、模糊逻辑、开发了人工神经网络和多目标遗传算法来模拟农民的行为，并根据在农场层面应用的灌溉深度的关税周期进行短期预测。在 Matlab 中开发的 CANGENFIS 被应用于位于西班牙西南部的一个真正的用水者协会。得到了用水户协会主要农作物的三个最优模型。可代表性的所有关税期间的平均值 (R² ) 和预测的准确度（标准误差预测，SEP）分别为 0.70、0.76% 和 0.85% 以及 19.9%、22.9% 和 19.5%，对于水稻、玉米和番茄作物模型。