Comparing ARIMA and various deep learning models for long-term water quality index forecasting in Dez River, Iran

Abstract

Water scarcity poses a significant global challenge, particularly in developing nations like Iran. Consequently, there is a pressing requirement for ongoing monitoring and prediction of water quality, utilizing advanced techniques characterized by low implementation costs, shorter timeframes, and high accuracy. In the present study, the investigation and forecasting of the monthly time series of a single-variable river water quality index have been addressed using ten water quality parameters. Daily monitoring data from four stations in the Dez River from 2010 to 2020 have been utilized to obtain the river water quality index value from the dataset. The Shannon entropy method has been employed to assign weights to each water quality parameter. Utilizing the integrated autoregressive integrated moving average (ARIMA) model, which ranks among the most extensively employed models for time series forecasting, and five deep learning models including Simple_RNN, LSTM, CNN, GRU, and MLP, the water quality index for the following year is predicted. The performance of the prediction models is evaluated using RMSE, MAE, MSE, and MAPE as evaluation metrics. The results indicate that the ARIMA model performs worse than the deep learning models, with the MSE, RMSE, MAE, and MAPE values for this model being 81.66, 9.037, 6.376, and 6.749, respectively. The deep learning models show results close to each other, demonstrating similar statistical index values. The outcomes of this study assist relevant decision-makers in planning and implementing necessary actions to enhance water quality, particularly freshwater resources in rivers.

Keywords: ARIMA; Deep learning; Dez River; EWQI; Time series; Water quality.