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Chakravarthy, S. R. S., Bharanidharan, N., Venkatesan, V. K., Abbas, M., Rajaguru, H., Mahesh, T. R. et al. (2023). Prediction of Water Quality Using Soft-Max-ELM Optimized Using Adaptive Crow-Search Algorithm. IEEE Access, 11, 140900-140913. https://doi.org/10.1109/access.2023.3339564

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• TITLE: Reliable Water Quality Prediction Using Bayesian Multi-Scale Convolutional Attention Network

  AUTHORS: Xiaolin Guo

  KEYWORDS: Uncertainty Quantification, Water Quality Prediction, Feature Fusion

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.13 No.3, March 31, 2025

  ABSTRACT: With the rapid development of industrialization and urbanization, the issue of water quality deterioration has become increasingly severe. Accurately assessing water quality is crucial for environmental protection and public health. Traditional water quality testing methods rely on sampling and laboratory analysis, which are costly and inefficient. In recent years, artificial intelligence (AI) based techniques have gained attention in research on water quality prediction because of their effectiveness and advanced capabilities. However, the black-box nature of AI model makes it difficult to quantify the reliability of their predictions, limiting their practical application. To address this issue, this paper proposes a Bayesian multi-scale convolutional attention network for water quality prediction. This method extracts high-level features affecting water quality through a multi-scale convolutional network and combines a self-attention mechanism and gated feature fusion approach to enhance the representation of key features and effectively integrate information. At the same time, Bayesian inference is used to generate prediction confidence intervals, providing a reliable assessment for the results. To the best of our knowledge, no research has yet combined Bayesian methods with deep learning for water quality prediction. Experimental results on the Kaggle water quality dataset demonstrate that the proposed method not only performs excellently in prediction accuracy but also effectively quantifies prediction uncertainty, providing scientific support for water quality assessment.