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

R. González Perea, E. Camacho Poyato and J.A. Rodríguez Díaz

Agricultural Water Management, 2021, vol. 256, issue C

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.

Keywords: Irrigation scheduling; Artificial intelligence; CANFIS; Genetic algorithm; Farmer’s behaviour modelling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377421003334
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:256:y:2021:i:c:s0378377421003334

DOI: 10.1016/j.agwat.2021.107068

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().