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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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A simulation/optimization model for selecting infrastructure alternatives in complex water resource systems.

Claudio Arena¹, Mario Rosario Mazzola, Giuseppe Scordo

¹Dipartimento di Ingegneria Idraulica ed Applicazioni Ambientali, Università di Palermo, Viale delle Scienze-Edificio 8, 90128 Palermo, Italy. arena@idra.unipa.it

Water Science and Technology : a Journal of the International Association on Water Pollution Research

|June 18, 2010

Summary

This study presents a simulation-optimization approach using genetic algorithms to select infrastructure alternatives for water supply systems. The method effectively balances costs and water availability, ensuring reliable resource management.

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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Water Resources Management
Environmental Engineering
Operations Research

Background:

Complex water resource systems require robust methods for infrastructure planning.
Multisource, multipurpose bulk water supply schemes face challenges in balancing supply and demand.
Selecting optimal infrastructure alternatives is crucial for system efficiency and reliability.

Purpose of the Study:

To introduce a simulation-optimization procedure for assessing and selecting infrastructure alternatives.
To develop a method for optimizing water resource systems using genetic algorithms.
To evaluate different objective functions and constraints for infrastructure planning.

Main Methods:

Utilized genetic algorithms to search for optimal infrastructure configurations (vector X).
Employed a simulation model to assess objective functions, including investment and operational costs.
Incorporated hydrologic inputs and system-specific rules for water allocation and deficit management.

Main Results:

The procedure successfully converged to optimal solutions for infrastructure selection.
Tested various objective functions, including cost-benefit analysis and scarcity cost incorporation.
Demonstrated the model's ability to align results with planning objectives in a real-world case study.

Conclusions:

The developed simulation-optimization procedure is effective for complex water resource system planning.
Genetic algorithms provide a powerful tool for optimizing infrastructure alternatives.
The approach offers a flexible framework for evaluating diverse planning scenarios and objectives.