Chen Tzu-Chia
College of Management and Design, Ming Chi University of Technology, New Taipei City, Taiwan, ROC E-mail:
Water Sci Technol. 2023 Mar;87(5):1294-1315. doi: 10.2166/wst.2023.047.
There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.
有几种水质参数建模方法，其中基于数据的方法是近几十年来的研究重点。当前研究旨在使用现代人工智能技术模拟水质参数，利用小波理论提高机器学习技术的性能，并将这些技术与其他广泛使用的机器学习技术进行比较。采用人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）对电导率（EC）、氯化物（Cl）、镁（Mg）和总溶解固体（TDS）水质参数进行建模。本研究的研究区域是台湾的高屏溪。在训练状态下，使用带有小波变换的混合模型将ANN模型的准确率在测试状态下从8.1%提高到22.5%，从25.7%提高到55.3%。此外，小波变换使ANFIS模型在训练状态下的准确率从6.7%提高到18.4%，在测试状态下从9.9%提高到50%。使用小波变换提高了机器学习模型结果的准确率。而且，小波-自适应神经模糊推理系统（WANFIS）模型优于小波-人工神经网络（WANN）模型，对所有四个水质参数都能进行更精确的建模。
