Prediction model of dissolved oxygen concentration in sewage treatment based on CNN-LSTM network and DBSCAN

Langlang Zhang; Pan Geng

doi:10.1117/12.3005791

10 October 2023 Prediction model of dissolved oxygen concentration in sewage treatment based on CNN-LSTM network and DBSCAN

Langlang Zhang, Pan Geng

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127993N (2023) https://doi.org/10.1117/12.3005791
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The water consumption of urban users is complex, and there are great differences between residential water and industrial wastewater. Real-time monitoring of water quality is an important task of urban water management. Among the water quality parameters, dissolved oxygen concentration (DO) is an important index to evaluate the quality of water. Therefore, the accuracy of dissolved oxygen prediction results is very important for the management of water bodies in sewage treatment. In this paper, a model based on DBSCAN-CNN-LSTM is proposed to solve the problems of inaccurate prediction accuracy and poor data convergence in the existing water quality prediction model in the sewage treatment process. Firstly, the obtained water quality data are preprocessed to obtain water quality factors with high correlation with DO.Secondly, the input data are predicted and output by establishing a hybrid model based on DBSCAN-CNN-GRU. The DBSCAN algorithm can cluster the sewage data and reduce the influence of local minima on the prediction results, so as to improve the prediction accuracy and data convergence. Compared with the traditional neural network models GRU, LSTM and CNN-LSTM, the predicted values is closer to the real values.

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

Citation Download Citation

Langlang Zhang and Pan Geng "Prediction model of dissolved oxygen concentration in sewage treatment based on CNN-LSTM network and DBSCAN", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127993N (10 October 2023); https://doi.org/10.1117/12.3005791

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KEYWORDS

Water quality

Data modeling

Systems modeling

Neural networks

Oxygen

Water

Artificial neural networks

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