Smart water management approach for resource allocation in High-Scale irrigation systems

Water Resources Management and Applications using GIS: An Overview

“…Model Builder and/or Python programming can be used to modify the GIS environment to fit with certain applications. The design and execution of an irrigation agent-based model (IABM) for the distribution of water in an irrigation district are presented by Jiménez et al in 2021. By employing 25% of the allowable level of moisture depletion relative to the total amount of accessible water in each field, this model avoids as much as possible that the soil water stress grows and satisfies the needs of the greatest number of agents.…”

On the Potential of a Smart Control Valve System for Irrigation Water Network Management

“…Furthermore, the response needs to be provided in realtime and rely on a distributed network of sensors/devices [6]. It should be also carefully considered that irrigation districts are complex systems and farmers are agents who have autonomy, intelligence, and relative knowledge about their environment [8]. Within this framework, the present study builds on recent works by the same research group (e.g., [9]), which discusses the potential associated with the introduction of a smart control valve, the green valve (GV) [10,11], in a pressurized irrigation distribution system (PIDS).…”

“…Furthermore, the response needs to be provided in realtime and rely on a distributed network of sensors/devices [6]. It should be also carefully considered that irrigation districts are complex systems and farmers are agents who have autonomy, intelligence, and relative knowledge about their environment [8].…”

Multi-Objective Decision Support for Irrigation Systems Based on Skyline Query

“…They first use the agricultural knowledge of professionals to calculate the objective values of different irrigation solutions and then apply decision support algorithms to obtain a small selection of optimal solutions. For example, Jim'enez et al [9] used the agronomic standards of each crop, such as crop coefficient, root depth, and soil water balance to evaluate the degrees of water wastage; a top-k query was then applied to select the solution with the least wastage. Cobbenhagen et al [10] employed various crop-growth models to estimate total crop profit and found the solutions with the most profit using top-k. More recently, Zhang et al [11] used the agronomic standards of the crops, soil water, and salt balance limits to construct a two-layer multi-objective agricultural water allocation model, which evaluates economic benefits and irrigation water productivity.…”