journal article·2022Open access

Model Identification and Control of Evapotranspiration for Irrigation Water Optimization

Wafa Difallah, Fateh Bounaama, Belkacem Draoui, Khelifa Benahmed, Abdelkader Laaboudi

Computers, Materials & Continua· Volume 70· Issue 1

Abstract

Water conservation starts from rationalizing irrigation, as it is the largest consumer of this vital source. Following the critical and urgent nature of this issue, several works have been proposed. The idea of most researchers is to develop irrigation management systems to meet the water needs of plants with optimal use of this resource. In fact, irrigation water requirement is only the amount of water that must be applied to compensate the evapotranspiration loss. Penman-Monteith equation is the most common formula to evaluate reference evapotranspiration, but it requires many factors that cannot be available in many cases. This leads to a trend towards behavior model estimation. System identification with control is one of the most promising applications in this axis. The idea behind this proposal depends on three stages: First, the estimation of reference evapotranspiration (ET0) by a linear ARX model, where temperature, relative humidity, insolation duration and wind speed are used as inputs, and ET0 estimated by Penman-Monteith equation as output. The results show that the values estimated by this method were in good agreement with the measured data. The second part of this paper is to manage the quantity of water. For this purpose, two controllers are used for testing, lead-lag and PID. To adjust the first controller and optimize the choice of its parameters, Nelder-Mead algorithm is used. In the last part, a comparative study is done between the two used controllers.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Plant Water Relations and Carbon Dynamics · Water resources management and optimization · Irrigation Practices and Water Management

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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