Water quality evaluation of water sources based on artificial neural network

Kun You; Jingrui Zhao; Xiaodan Wang

doi:10.1117/12.3013329

29 November 2023 Water quality evaluation of water sources based on artificial neural network

Kun You, Jingrui Zhao, Xiaodan Wang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 1293718 (2023) https://doi.org/10.1117/12.3013329
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

A 14×6×5 BP artificial neural network model was constructed using Matlab software. The model was trained with 400 samples, resulting in a training error of only 1.67×10^-7, which was significantly lower than the target training error 1×10^-6. Simultaneously, a test set of 100 samples was used to evaluate the model's performance, and it was found that 100% accuracy, recall, precision, and F1 score were achieved, indicating that groundwater quality could be accurately and swiftly evaluated. The results of 176 samples from 2020 to 2022 were analyzed, revealing that the area's groundwater quality was primarily categorized as Class II and Class III. During 2020-2022, the proportion of water wells evaluated as Class II and Class III was 97%, 96%, and 98%, respectively. The primary factors affecting the groundwater quality in this region were identified as the iron and manganese indicators. The BP neural network method allowed for the retention of all data information. Based on all indicators, the water quality was assessed and the non-linear relationships between data were processed to ensure the objectivity and accuracy of evaluation results.

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

Citation Download Citation

Kun You, Jingrui Zhao, and Xiaodan Wang "Water quality evaluation of water sources based on artificial neural network", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 1293718 (29 November 2023); https://doi.org/10.1117/12.3013329

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KEYWORDS

Artificial neural networks

Neurons

Water quality

Statistical modeling

Performance modeling

Statistical analysis

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