An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices.

Aiming at the shortcomings of a single machine learning model with low model prediction accuracy and insufficient generalization ability in house price index prediction, a whale algorithm optimized support vector regression model based on bagging ensemble learning method is proposed. Firstly, gray correlation analysis is used to obtain the main influencing factors of house prices, and the segmentation forecasting method is used to divide the data set and forecast the house prices in the coming year using the data of the past ten years. Secondly, the whale optimization algorithm is used to find the optimal parameters of the penalty factor and kernel function in the SVR model, and then, the WOA-SVR model is established. Finally, in order to further improve the model generalization capability, a bagging integration strategy is used to further integrate and optimize the WOA-SVR model. The experiments are conducted to forecast the house price indices of four regions, Beijing, Shanghai, Tianjin, and Chongqing, respectively, and the results show that the prediction accuracy of the proposed integrated model is better than the comparison model in all cases.

The emission peak and carbon neutrality targets pose a great challenge to carbon emission reduction in the coal industry, and the coal industry will face an all-around deep adjustment. The forecast of coal price is crucial for reducing carbon emissions in the coal industry in an orderly manner under the premise of ensuring national energy security. The volatility and instability of coal prices are a result of multiple influencing factors, making it very difficult to make accurate predictions of coal price changes. We propose in this paper an innovative hybrid forecasting method (CEEMDAN-GWO-CatBoost) for forecasting coal price indexes by combining machine learning models, feature selections, data decomposition, and model interpretation. By combining high forecasting accuracy with good interpretability, this method fills a gap in the field of coal price forecasting. Initially, we examine the factors that influence coal prices from five angles: Supply, demand, macroeconomic factors, freight costs, and substitutes; and we employ Spearman correlation analysis to reduce the complexity of the attribute set and devise a coal price forecasting index system. Secondly, the CEEMDAN method is used to decompose the raw coal price index data into seven intrinsic modal functions and one residual term in order to weaken the volatility of the data caused by complex factors. Next, the CatBoost model hyperparameters are optimized using the Grey Wolf Optimizer algorithm, while the coal price data is fed into the combined forecasting model. Lastly, the SHAP interpretation method is introduced for studying the important indicators affecting coal prices. The experimental results show that the combined CEEMDAN-GWO-CatBoost forecasting model proposed in this paper has significantly better forecasting performance than other comparative models, and the SHAP method employed in this study identifies the macroeconomic environment, freight costs, and coal import volume as significant factors affecting coal prices. As part of the contribution of this paper, specific recommendations are made to the government regarding the formulation of a regulatory policy for the coal industry in the context of carbon neutrality based on the findings of this research.

Toward solving the slow convergence and low prediction accuracy problems associated with XGBoost in COVID-19-based transmission prediction, a novel algorithm based on guided aggregation is presented to optimize the XGBoost prediction model. In this study, we collect the early COVID-19 propagation data using web crawling techniques and use the Lasso algorithm to select the important attributes to simplify the attribute set. Moreover, to improve the global exploration and local mining capability of the grey wolf optimization (GWO) algorithm, a backward learning strategy has been introduced, and a chaotic search operator has been designed to improve GWO. In the end, the hyperparameters of XGBoost are continuously optimized using COLGWO in an iterative process, and Bagging is employed as a method of integrating the prediction effect of the COLGWO-XGBoost model optimization. The experiments, firstly, compared the search means and standard deviations of four search algorithms for eight standard test functions, and then, they compared and analyzed the prediction effects of fourteen models based on the COVID-19 web search data collected in China. Results show that the improved grey wolf algorithm has excellent performance benefits and that the combined model with integrated learning has good prediction ability. It demonstrates that the use of network search data in the early spread of COVID-19 can complement the historical information, and the combined model can be further extended to be applied to other prevention and control early warning tasks of public emergencies.

The zero-waste city is one of the most visionary initiatives for solving waste problems by enhancing the life cycle solid waste management system. However, the development level of ZW city construction remains unclear. Resource-based areas tend to produce large amounts of industrial solid waste, and achieving a zero-waste city is difficult due to insufficient resource utilization capacity. This study proposed a practical integrated MCDM approach to assess the performance of the ZW city and applied the approach to a typical coal resource-based province in China. The spatiotemporal characteristics and factors influencing the performance of zero-waste cities in each city were further explored. The performance levels increased during the study period; however, the growth rate of most cities is slow. Spatially, the performance levels of zero-waste cities gradually decreased from south to north, showing a radiation pattern with Taiyuan at its core. Challenges toward zero-waste city development were further identified, including heavy industrial structure, widespread underutilization of industrial solid waste, inadequate management of hazardous solid waste, low rate of urban domestic waste classification, and ineffective treatment. The approach gives a holistic and broader picture of the zero-waste management performance, which enables us to identify the challenges in promoting zero-waste cities.