Multipoint Pollution Localization Method for Surface Water Based on Time-Frequency Analysis

Abstract

The localization of surface water pollution involves the inversion of source parameters such as positions, amounts, and duration based on monitoring data. This process is crucial for formulating remedial strategies and identifying responsible parties during sudden water pollution incidents. Existing models target single-source localization but fail in multisource scenarios with continuous, mixed discharges, as concentration superposition at monitoring points distorts dispersion models, causing nonunique solutions, significant deviations, and degraded accuracy. This study proposes a time-frequency analysis method to address multipoint surface water pollution source localization under complex scenarios involving instantaneous and continuous discharges, transforming the hydrodynamic inverse problem into a time-domain simulation and frequency-domain optimization framework. It constructs a discrete convolution dynamic equation to uniformly model forward diffusion process. Coupled with the Fourier frequency-domain mapping and the position regularization loss function, the method employs the African Vultures Optimization Algorithm (AVOA) for parallel optimization, achieving high-precision inversion of source parameters for pollution localization. Experimental results demonstrate relative errors below 10% for all parameters in single- and multisource scenarios, and validation on the U.S. Truckee river tracer data set achieved a coefficient of determination (R²) exceeding 0.95, confirming the method’s reliability for practical applications.