Research on a Neural Network-Based Method for Detecting the Concentration and Particle Size of Suspended Solids Based on Multi-Frequency Acoustic Information

Suspended solids (SS) composed of micrometer-to-nanometer-scale particles, including silt and organic matter, significantly impact aquatic ecosystems through physicochemical interactions. Accurate monitoring of SS concentration and particle size is critical for environmental protection and pollution prevention. We constructed multiple datasets using received signals after propagation through different aqueous environments. Analysis of the performance of neural networks across different datasets revealed that high-frequency signals with rich spectra have high potential for detecting suspended solid information in complex aqueous environments. Our study explores the performance of two neural networks (Conv1dBGRU and TCN) in combination with channel attention mechanisms in classification tasks focused on the concentration of suspended solids and particle size. We also constructed neural networks for multi-task learning using both hard and soft parameter-sharing methods to simultaneously complete the classification tasks for concentration and particle size. The results show that multi-frequency acoustic signals in combination with neural networks can achieve simultaneous and accurate estimation of the concentration of suspended solids and particle size.