During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( and ). This kind of promising research can contribute significantly to water management.

中文翻译：

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使用人工智能算法进行水质预测

近年来，水质受到多种污染物的威胁。因此，水质建模和预测对于控制水污染变得非常重要。在这项工作中，开发了先进的人工智能（AI）算法来预测水质指数（WQI）和水质分类（WQC）。对于WQI预测，已经开发了人工神经网络模型，即非线性自回归神经网络（NARNET）和长短期记忆（LSTM）深度学习算法。此外，WQC预​​测还使用了三种机器学习算法，即支持向量机（SVM）、最近邻（K-NN）和朴素贝叶斯。使用的数据集有 7 个重要参数，并且根据一些统计参数对开发的模型进行了评估。结果表明，所提出的模型能够准确预测 WQI 并根据优异的鲁棒性对水质进行分类。预测结果表明，NARNET 模型在 WQI 值的预测方面略优于 LSTM，并且 SVM 算法在 WQC 预测方面取得了最高的准确率（97.01%）。此外，NARNET 和 LSTM 模型在测试阶段达到了相似的精度，但回归系数略有不同（和）。这种有前景的研究可以为水资源管理做出重大贡献。