Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep learning models. All prediction algorithms, except for the ARIMA model working on a single variable, were tested with univariate inputs consisting of one of two dependent variables as well as multivariate inputs containing both dependent and independent variables. We found that deep learning models (6.31-18.78%, in terms of the mean absolute percentage error) showed better performance than the ARIMA model (27.32-404.54%) in univariate data sets, regardless of dependent variables. However, the accuracy of prediction was not improved for all dependent variables in the presence of other associated water quality variables. In addition, changes in the number of input variables, sliding window size (i.e., input and output time steps), and relevant variables (e.g., meteorological and discharge parameters) resulted in wide variation of the predictive accuracy of deep learning models, reaching as high as 377.97%. Therefore, a refined search identifying the optimal values on such influencing factors is recommended to achieve the best performance of any deep learning model in given multivariate data sets.

Automated summary

This study found that deep learning models outperformed the ARIMA model in univariate surface water quality forecasting, but the presence of other related factors in multivariate datasets impacted prediction accuracy, requiring a refined search for optimal values to achieve the best performance.