Inversion of water quality parameters of surface water source based on BP neural network optimized by improved PSO

Xinyu Chai; Xianwen Wu; Xiaohui Chen; Yanan Yu

doi:10.1117/12.3004990

9 October 2023 Inversion of water quality parameters of surface water source based on BP neural network optimized by improved PSO

Xinyu Chai, Xianwen Wu, Xiaohui Chen, Yanan Yu

Author Affiliations +

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910Y (2023) https://doi.org/10.1117/12.3004990
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Water is an important resource for human survival. It has become an urgent problem for people to obtain large-scale and real-time water change information. The surface water source area of Hunchun city is selected as the study area. Combined with the field measured data and quasi synchronous landsat8 data, the water quality inversion models of total nitrogen, total phosphorus and chlorophyll a in the study area are established based on BP neural network and improved PSO-BP neural network. Nine test points are randomly selected to evaluate the accuracy of the two models.The results show that: (1) The precision of improved PSO-BP neural network model is better than that of BP neural network. (2) In general, the water quality concentration of Hunchun surface water source area is different. (3) The total nitrogen in Hunchun surface water source is mainly Class II water quality, the total phosphorus is mainly Class IV water quality, and the chlorophyll a is Class I and Class II water quality. It can be seen that the inversion effect of surface water quality parameters based on improved PSO-BP neural network technology is better than that of BP neural network.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyu Chai, Xianwen Wu, Xiaohui Chen, and Yanan Yu "Inversion of water quality parameters of surface water source based on BP neural network optimized by improved PSO", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910Y (9 October 2023); https://doi.org/10.1117/12.3004990

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