An auto-adaptive optimization approach for targeting nonpoint source pollution control practices

Abstract

To solve computationally intensive and technically complex control of nonpoint source pollution, the traditional genetic algorithm was modified into an auto-adaptive pattern, and a new framework was proposed by integrating this new algorithm with a watershed model and an economic module. Although conceptually simple and comprehensive, the proposed algorithm would search automatically for those Pareto-optimality solutions without a complex calibration of optimization parameters. The model was applied in a case study in a typical watershed of the Three Gorges Reservoir area, China. The results indicated that the evolutionary process of optimization was improved due to the incorporation of auto-adaptive parameters. In addition, the proposed algorithm outperformed the state-of-the-art existing algorithms in terms of convergence ability and computational efficiency. At the same cost level, solutions with greater pollutant reductions could be identified. From a scientific viewpoint, the proposed algorithm could be extended to other watersheds to provide cost-effective configurations of BMPs.

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

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Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system

“…The automatic photochemical flow analysis and monitoring system can monitor non-point source pollution without affecting the mechanized farming of farmland. The automatic photochemical flow analysis and monitoring system can eliminate the lag and uncertainty of the detection results of traditional detection methods, and understand the impact of ANPS pollution on agricultural ecosystem in the context of climate change more comprehensively and accurately 25 , 26 .…”

A GIS‐based Upscaling Estimation of Nutrient Runoff Losses from Rice Paddy Fields to a Regional Level

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