Water quality inversion in the Yellow River Basin of Ningxia based on TSO-LSSVM model

Xiaojun Li; Zhonghua Guo; Qi Li; Bo Ban

doi:10.1117/12.3049996

8 November 2024 Water quality inversion in the Yellow River Basin of Ningxia based on TSO-LSSVM model

Xiaojun Li, Zhonghua Guo, Qi Li, Bo Ban

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163A (2024) https://doi.org/10.1117/12.3049996
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

The real-time detection of water quality is of paramount importance for the maintenance of ecological balance and the promotion of regional development. In this study, the data obtained from 10 sampling points in the Yellow River Basin of Ningxia and Landsat 8 images were selected for inclusion in the dataset. The tuna algorithm (TSO) was employed to optimize the neural network of the least squares support vector machine (LSSVM), resulting in the construction of the TSO-LSSVM model. This model was utilized to invert turbidity (TUB), electrical conductivity (EC), dissolved oxygen (DO), and total nitrogen (TN), and was then compared with the LSSVM model. The results show that the TSO-LSSVM model has stronger global search ability, and the R² of TUB, EC, DO, and TN is 0.84878, 0.6756, 0.62336, and 0.57783, respectively, and the inversion accuracy is improved by 2.922%, 3.24%, 1.763%, and 5.568% compared with the LSSVM neural network, respectively. The TSO-LSSVM model constructed in this paper demonstrates efficacy in the water quality inversion of the Yellow River Basin in Ningxia, offering a novel reference for the prediction of complex inland river water quality parameters.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaojun Li, Zhonghua Guo, Qi Li, and Bo Ban "Water quality inversion in the Yellow River Basin of Ningxia based on TSO-LSSVM model", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163A (8 November 2024); https://doi.org/10.1117/12.3049996

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KEYWORDS

Water quality

Atmospheric modeling

Data modeling

Mathematical optimization

Oxygen

Landsat

Turbidity

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