This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread.

On June 29 2021, the World Health Organization (WHO) reported around 45,951 confirmed cases and 817 deaths of COVID-19 in India, and 64,903 confirmed cases and 1,839 deaths in Brazil. This virus has been determined as a global pandemic by WHO. Accurate forecast of COVID-19 cases has become a crucial task in the decision-making of hospital managers to optimally manage the available resources and staff. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization (BO) was used to forecast the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. Specifically, the BO algorithm is employed to find the optimal hyperparameters of the GPR model to improve the forecasting quality. We compared the performance of the Optimized GPR with 14 models, including Support vector regression with different kernels, GPR with different kernels, Boosted trees, and Bagged trees. We also applied the BO to the other investigated predictors to maximize their forecasting accuracy. Three statistical criteria are used for the comparison. The daily records of confirmed and recovered cases from Brazil and India are adopted in this study. Results reveal the high performance of the GPR models compared to the other models.

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates and compares a set of 23 candidate machine-learning models to predict WWTP energy consumption using actual data from the Melbourne WWTP. To this end, Bayesian optimization has been applied to calibrate the investigated machine learning models. Random Forest and XGBoost (eXtreme Gradient Boosting) were applied to assess how the incorporated features influenced the energy consumption prediction. In addition, this study investigated the consideration of information from past data in improving prediction accuracy by incorporating time-lagged measurements. Results showed that the dynamic models using time-lagged data outperformed the static and reduced machine learning models. The study shows that including lagged measurements in the model improves prediction accuracy, and the results indicate that the dynamic K-nearest neighbors model dominates state-of-the-art methods by reaching promising energy consumption predictions.

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