Hybrid decision tree-based machine learning models for short-term water quality prediction

Water resources are the foundation of people’s life and economic development, and are closely related to health and the environment. Accurate prediction of water quality is the key to improving water management and pollution control. In this paper, two novel hybrid decision tree-based machine learning models are proposed to obtain more accurate short-term water quality prediction results. The basic models of the two hybrid models are extreme gradient boosting (XGBoost) and random forest (RF), which respectively introduce an advanced data denoising technique - complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Taking the water resources of Gales Creek site in Tualatin River (one of the most polluted rivers in the world) Basin as an example, a total of 1875 data (hourly data) from May 1, 2019 to July 20, 2019 are collected. Two hybrid models are used to predict six water quality indicators, including water temperature, dissolved oxygen, pH value, specific conductance, turbidity, and fluorescent dissolved organic matter. Six error metrics are introduced as the basis of performance evaluation, and the results of the two models are compared with the other four conventional models. The results reveal that: (1) CEEMDAN-RF performs best in the prediction of temperature, dissolved oxygen and specific conductance, the mean absolute percentage errors (MAPEs) are 0.69%, 1.05%, and 0.90%, respectively. CEEMDAN-XGBoost performs best in the prediction of pH value, turbidity, and fluorescent dissolved organic matter, the MAPEs are 0.27%, 14.94%, and 1.59%, respectively. (2) The average MAPEs of CEEMDAN-RF and CEEMMDAN-XGBoost models are the smallest, which are 3.90% and 3.71% respectively, indicating that their overall prediction performance is the best. In addition, the stability of the prediction model is also discussed in this paper. The analysis shows that the prediction stability of CEEMDAN-RF and CEEMDAN-XGBoost is higher than other benchmark models. •Two hybrid decision tree-based models are proposed to predict the water quality.•An advanced denoising method is used to preprocess raw data.•The case study was conducted on the most polluted river Tualatin River in Oregon, USA.•The prediction stability of the model is analyzed.