# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 1 Introduction
### Literature Review  
Mathematical models can be used to forecast the amount of waste and the amount of energy produced based on various, often non-linear, factors affecting the combustion or fermentation process of municipal waste. Various machine learning models were used,including artificial neural networks (ANNs), regression trees, multi-dimensional adaptive regression, random forest, the theory of roughing harvest, reinforced regression, and combined methods for forecasting the amount of municipal waste. [PERSON] et al. (2021) conducted a comparison of six machine learning models for municipal solid waste prediction (MSW) in China [11]. The models included Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBOOST), K-Nearest Neighbor (KNN), and Artificial Neural Network. The study also examined the correlation between variables based on past entries. The results confirm high prediction accuracy for all machine learning models, with the ANN model performing the best. The data include Total Regional Gross Domestic Product (GDP), Value Added by Transportation, Warehouse, and Postal Services, Wholesale and Retail Value Added, Value Added by the Accommodation and Catering Industry, City Area, Urban Population Density, the Number of Urban Populations, Urban Per Capita Disposable Income, and Total Retail Sales of Consumer Goods. The regional GDP indicator is widely acknowledged as the most significant variable in predicting waste production [11]. In the authors' previous study, a Neural Network was employed for predicting the volume of municipal solid waste in Poland. [PERSON] and [PERSON] utilized neural network modeling to estimate the quantities of MSW in Poland, categorizing the waste into five types: glass, biodegradable materials, paper and cardboard, plastics and metals, and other assorted waste [12]. The proposed models integrated various explanatory variables to assess the impact of economic, demographic, and social factors on waste generation volumes. The neural network models varied by adjusting the number of hidden neurons between 2 and 10. The performance of these models was evaluated based on the Pearson correlation coefficient (R) and the mean squared error (MSE). The ANN model featuring six hidden neurons demonstrated high predictive accuracy, evidenced by a significant R-value (0.914) for the categorized waste types. When analyzing statistical data from 2013 to 2019, it was found that, the model predicted a 2% increase in waste production by 2024. These results confirm the efficiency of the ANN model as a cost-effective approach for designing integrated waste management systems [12]. A significant parameter for waste energy assessment is the chemical properties of waste, particularly the Higher Heating Value (HHV). Waste used to obtain energy must be characterized by a high HHV [13]. Determining the higher heating value of waste can be challenging due to the heterogeneity of the material and the precision of measuring methods. This can make it difficult to obtain accurate and representative results. Advanced analytical methods are required due to differences in humidity, chemical composition, and waste pollution. Prior to designing installations for efficient and optimized waste processing, it is advisable to create mathematical models to forecast the higher fuel value of waste. Accurate HHV forecasting enables minimization of waste directed to landfills by facilitating effective energy utilization. Models also aid in identifying waste with the highest energy value, thereby enabling better selection and segregation of materials prior to processing. Accurate forecasting of the higher heating value enables adjustment of combustion process parameters, including temperature, air flow, or combustion time. This enhances energy efficiency and reduces harmful substance emissions [14]. Several studies have developed models to estimate HHV accurately for various types of MSW.