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 using a consensus‐based multiple information fusion method. Multiple water quality information sources are fused in the framework via spatial clustering and temporal Bayesian updating. Considering the real‐world complexity, the recession‐curve displacement method is used to handle multi‐sources scenario to enhance the accuracy of the framework. Meanwhile, a coupled forward‐inverse model instead of random sampling is used to improve the solving efficiency of PSD. The framework is validated by a real water pollution event and a number of semi‐hypothetical case studies. The results show that the prediction accuracy of the single‐source and double‐source pollution problems are 88.40% and 82.69%, with an average relative error of 9.75% and 11.42%, respectively. In addition, the uncertainty contribution quantified by a variance decomposition approach suggests that the interaction between parameter and measurement uncertainties has the greatest impact on the PSD results. The results reveal the advantages of the proposed decision framework for government organizations and communities involved in water environment management.

Traceability of River Water Pollution Based on MFO and M-H Algorithms

“…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.…”