The performance of classification and forecasting Dong Nai River water quality for sustainable water resources management using neural network techniques

This study has classified and predicted the water quality for the Dong Nai river at different times. The data mining techniques were used to screen out unreliable data. The neural network model FFNN in the form of architecture I(8)-HL(9)-O(1) has been successfully developed to classify the water quality of the Dong Nai river during the period from 2012 to 2019. For this model, the RMSE values for the training, validation, and test sets are equal to 0.0392, 0.0411, and 0.0423, respectively. Also, the MAPE values for those data sets are 1.241, 1.444, and 1.511, respectively. The models ARIMA(0,1,3)(1,1,1), NAR, NAR-MA, LSTM, and LSTM-MA were used to forecast the water quality according to different time series (months) k = 12 and k = 24 at the monitoring points. The LSTM-MA hybrid model provided faster training time and more reliable forecasts than the ARIMA, NAR, NAR-MA, and LSTM models. The RMSE and MAPE values of the LSTM-MA hybrid model for predicting the forecast set are smaller than those from other models. The LSTM-MA hybrid model was used to classify the water quality over the years 2020 to 2022. The water quality classification derived from the LSTM-MA hybrid model in close agreement with the actual monitoring data. This model becomes reliable and useful for the water quality classification of the Dong Nai River.