Water Quality Prediction Using Artificial Intelligence Algorithms
Aldhyani, Theyazn H. H . 2020
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), K-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
(RNARNET = 96:17% and RLSTM = 94:21%). This kind of promising research can contribute significantly to water management
