Research and Application of Bp Neural Network in Water Quality Testing

DOI

10.2991/978-94-6463-512-6_74How to use a DOI?

Keywords

Back Propagation Neural Network; Gradient Boosting Decision Tree; Water Quality Detection

Abstract

To ensure sustainable use of water resources and protect a robust ecological environment, developing effective and precise models for assessing water quality is essential. This research focuses on training detection models using water sample data collected in India. After preprocessing the data and constructing models using various methodologies, optimal model parameters were chosen by evaluating different hidden layers and neuron configurations to improve the model's learning capacity. Despite the suboptimal performance of the Back Propagation (BP) neural network with small-scale and weakly correlated data, parameter adjustments and suitable activation functions enhanced training effectiveness. Additionally, the training outcomes of alternative machine learning models on the same dataset were compared after training the BP neural network. The results demonstrate that gradient boosting trees exhibit superior performance under similar conditions, underscoring the critical importance of selecting appropriate models based on data characteristics. Specifically, when applied to small-scale datasets, experimental results using Gradient Boosting Decision Trees significantly outperform those obtained with BP neural networks, thereby effectively enhancing water quality detection models' accuracy.In utilizing a model recognized for its exceptional precision, this investigation revealed that relying on assessment criteria as markers for water quality analysis yielded less than optimal results.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lingxi Zeng
PY  - 2024
DA  - 2024/09/23
TI  - Research and Application of Bp Neural Network in Water Quality Testing
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 704
EP  - 710
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_74
DO  - 10.2991/978-94-6463-512-6_74
ID  - Zeng2024
ER  -