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Updated: May 12, 2026

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

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

Published on: December 9, 2012

A stochastic optimization approach for integrated urban water resource planning.

Y Huang¹, J Chen, S Zeng

¹School of Environment, Tsinghua University, Beijing, 100084, China.

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

|April 5, 2013

Summary

Urban water resource planning must integrate conventional and nonconventional sources, like reclaimed wastewater and harvested rainwater, to address scarcity and uncertain demand. An optimization model shows cost-effective allocation and improved reliability for sustainable urban water 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

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Last Updated: May 12, 2026

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

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

Published on: December 9, 2012

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Environmental Science
Water Resource Management
Urban Planning

Background:

Urban water systems face increasing scarcity and quality degradation.
Rapid urbanization and economic growth create uncertain water demand and fragile infrastructure.
Nonconventional water resources (reclaimed wastewater, harvested rainwater) are crucial for urban water planning.

Purpose of the Study:

To develop an integrated nonlinear stochastic optimization model for urban water resource evaluation and planning.
To optimize urban water flows considering both quantity and quality from diverse sources and uses.
To enhance the reliability of urban water systems under future uncertainties.

Main Methods:

Developed an integrated nonlinear stochastic optimization model.
Accounted for water quantity, water quality, source diversity, usage, and costs.
Applied the model to a case study in Beijing facing significant water shortage.

Main Results:

Demonstrated cost-effective allocation strategies for various urban water resources.
Quantified the impact of different planning alternatives on resource reliability.
Identified optimal water flow management under uncertainty.

Conclusions:

Integrated planning of conventional and nonconventional water resources is essential for urban water security.
The developed optimization model provides a robust framework for reliable urban water resource management.
Effective resource allocation can mitigate water scarcity challenges in rapidly urbanizing areas.