# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 2 Materials and Methods
* Gross domestic product at market prices measures the value of all goods and services produced in a country at a given time, usually during the year, at market prices. High GDP can indicate a higher demand for energy due to increased economic activity, which in turn can stimulate investment in energy production, especially in new technologies and energy sources. * Recycling rate of municipal waste refers to the percentage of municipal waste that is recycled out of the total amount of municipal waste generated. A higher recycling indicator can help reduce the demand for primary raw materials, which in turn can reduce the energy required to extract and process these raw materials. In addition, recycling of some materials, such as metals, may use less energy than the production of primary raw materials, which also has an impact on the overall demand for energy. * Domestic material consumption per capita is an indicator that measures the amount of materials (expressed in tons or kilos per person) consumed in the country's economy per capita. Higher DMC may indicate greater consumption of natural resources and the associated higher energy demand for extracting, processing and transporting these materials. * Environmental tax revenue is the revenue received by government from taxes levied on business activities and products that have a negative impact on the environment. Introducing and increasing these taxes can encourage investment in cleaner technologies and more efficient use of energy, thereby reducing demand for traditional, more polluting energy sources. This revenue can also be used to finance renewable energy and energy efficiency projects, thus supporting the transition to more sustainable energy systems. * Share of energy from renewable sources is the percentage of energy produced from renewable sources (such as solar, wind, geothermal, hydropower, biomass, and ocean energy) in the total energy consumption of a given region, country or the world [21]. The output data were energy recovery from waste. Energy recovery is a waste management method that converts the energy contained in waste into useful energy in the form of heat, electricity or fuel through various processes such as combustion, pyrolysis, gassing or methane fermentation [15]. The selection of ElasticNet, Decision Trees, and Neural Network models was based on the need to evaluate the most effective method for energy recovery prediction from municipal solid waste (MSW) using a limited dataset of 225 entries. These methods were chosen for their unique strengths and ability to handle small datasets, which are critical in accurately predicting outcomes where data are scarce. The models were performed using the Matlab (Version R2023a) software with the Neural Network App. ElasticNet is advantageous for the datasets where predictors are many or highly correlated, as it combines L1 and L2 regularization to enhance model performance and prevent overfitting. Decision Trees were selected for their simplicity and interpretability, which is particularly useful for stakeholders to understand the decision paths. Neural Networks are adept at capturing complex, non-linear patterns in data, making them suitable for modeling the intricate processes involved in energy recovery. The juxtaposition of these methodologies serves a comparative purpose to identify which algorithm is best suited for predicting with precision and reliability, given the data constraints and the intricate nature of the energy recovery processes from MSW. The evaluation of the performance of these models can provide insights into their robustness, accuracy, and utility in the application of waste-to-energy conversion.