A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

A novel RF-CEEMD-LSTM model for predicting water pollution

Accurate water pollution prediction is an important basis for water environment prevention and control. The uncertainty of input variables and the nonstationary and nonlinear characteristics of water pollution series hinder the accuracy and reliability of water pollution prediction. This study proposed a novel water pollution prediction model (RF-CEEMD-LSTM) to improve the performance of water pollution prediction by combining advantages of the random forest (RF) and Long short-term memory (LSTM) models and Complementary ensemble empirical mode decomposition (CEEMD). The experimental results based on measured data show that the proposed RF-CEEMD-LSTM model can accurately predict water pollution trends, with a mean ab-solute percentage error (MAPE) of less than 8%. The RMSE of the RF-CEEMD-LSTM model is reduced by 62.6%, 39.9%, and 15.5% compared to those of the LSTM, RF-LSTM, and CEEMD-LSTM models, respectively, proving that the proposed method has good advantages in predicting non-linear and nonstationary water pollution sequences. The driving force analysis results showed that TN has the most significant impact on water pollution prediction. The research results could provide references for identifying and explaining water pollution variables and improving water pollution prediction method.

Analytical Investigation of the Impact of Jet Geometry on Aeration Effectiveness Using Soft Computing Techniques

Jet aeration is a commonly used technique for introducing air into water during wastewater treatment. In this investigation, the efficacy of different soft computing models, namely, Random Forest, Reduced Error Pruning Tree, Artificial Neural Network (ANN), Gaussian Process, and Support Vector Machine, was examined in predicting the aeration efficiency (E20) of circular and square jet configurations in an open channel flow. A total of 126 experimental data points were utilized to develop and validate these models. To assess the models' performance, three goodness-of-fit parameters were employed: correlation coefficient (CC), root-mean-square error (RMSE), and mean absolute error (MAE). The analysis revealed that all of the developed models exhibited predictive capabilities, with CC values surpassing 0.8. Nonetheless, when it comes to predicting E20, the ANN model outperformed other soft computing models, achieving a CC of 0.9748, MAE of 0.0164, and RMSE of 0.0211. A sensitivity analysis emphasized that the angle of inclination exerted the most significant influence on the aeration in an open channel. Furthermore, the results demonstrated that square jets delivered superior aeration compared to that of circular jets under identical operating conditions.

Progress and opportunities in advancing near-term forecasting of freshwater quality

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.