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

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An almost-parameter-free harmony search algorithm for groundwater pollution source identification.

Simin Jiang¹, Yali Zhang¹, Pei Wang²

¹Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China

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

|December 17, 2013

Summary

Identifying unknown groundwater pollution sources is crucial. This study introduces a novel simulation-optimization approach using a harmony search algorithm for accurate source identification, even with data challenges.

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Last Updated: May 4, 2026

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

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Published on: December 9, 2012

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

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Published on: September 26, 2017

Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Area of Science:

Environmental Science
Hydrogeology
Computational Modeling

Background:

Groundwater pollution source identification is a complex environmental challenge.
Accurate characterization of contaminant origins is vital for effective remediation strategies.
Existing methods often struggle with data limitations and complex site conditions.

Purpose of the Study:

To develop and evaluate a simulation-optimization approach for identifying unknown groundwater pollution sources.
To introduce an almost-parameter-free harmony search algorithm tailored for this environmental problem.
To assess the methodology's robustness against irregular geometry, erroneous data, and limited prior information.

Main Methods:

Coupling a contaminant transport simulation model with a heuristic harmony search algorithm.
Developing a novel, almost-parameter-free harmony search algorithm.
Applying the integrated methodology to an illustrative groundwater pollution source identification case.

Main Results:

The proposed methodology successfully identified unknown pollution sources in a test case.
Satisfactory estimations were achieved despite challenges like irregular geometry and erroneous monitoring data.
The harmony search algorithm demonstrated effectiveness even with a shortage of prior information on potential source locations.

Conclusions:

The simulation-optimization approach, particularly with the developed harmony search algorithm, provides a robust tool for groundwater pollution source identification.
This method offers reliable estimations in complex real-world scenarios.
The findings contribute to improved environmental management and groundwater protection strategies.