2023
DOI: 10.1029/2022wr032866
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A Decision Support Framework for Pollution Source Detection via Coupled Forward‐Inverse Optimization and Multi‐Information Fusion

Abstract: Due to the uncertainty in sensor data, low model accuracy, and high parameter heterogeneity in water quality modeling, pollution source detection (PSD) typically results in a problem of multiple possible solutions, which is the so‐called non‐uniqueness effect. Identifying unique solution to PSD problems is fundamentally essential for water quality control in surface water and groundwater systems. This study proposes a decision support framework to reduce the impact of uncertainty and identify a unique solution… Show more

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Cited by 4 publications
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“…Based on this algorithm, the parameter calibration can be improved. Besides, the "inverse problem" modeling process of the water pollution migration and diffusion model is easily affected by uncertain factors such as water quality monitoring data, model parameters, and water pollution event information [29]. Bayesian estimation can deal with the uncertainty well.…”
Section: Introductionmentioning
confidence: 99%
“…Based on this algorithm, the parameter calibration can be improved. Besides, the "inverse problem" modeling process of the water pollution migration and diffusion model is easily affected by uncertain factors such as water quality monitoring data, model parameters, and water pollution event information [29]. Bayesian estimation can deal with the uncertainty well.…”
Section: Introductionmentioning
confidence: 99%