Garbage prediction using regression analysis for municipal corporations of Indian cities - Raj Kumar Sharma; Manisha Jailia (Cognitive Computation and Systems)
Abstract Garbage management is exceptionally critical and poses enormous environmental challenges. It has always been a vital issue in municipal corporations. However, municipal agencies have developed and used garbage management systems. Garbage forecasting still plays a crucial role in the management system and helps improve or create a garbage management system. This research examines the information from 212 cities to suggest a helpful regression model for garbage forecasting and control. To establish a connection between the variables, the descriptive study employs statistical techniques to learn about the composition of data collected from municipal corporations and conduct correlation analysis. Population and garbage depend highly on one another, as evidenced by their correlation coefficient of 0.922,144. The primary research is used to build an alternate hypothesis that shows the chosen variables are highly dependent on one another. The dataset is scaled and divided into a training and testing 80:20 ratio during the pre‐processing data phase. This research aims to do a regression analysis with daily garbage production, urban area, and population as independent variables. This research initiates a variety of regression models, including multiple linear regression (MLR), artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). The MLR model's R2 value of 0.85 indicates that it has the potential to accurately forecast daily garbage production based on just two independent variables and a single dependent variable. Random Forest Regression (RFR) with (MSE: 100,078.749 & MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. The estimated results from each model are compared to the test data values on line graphs and Taylor plots. The mean square error and the mean absolute error in the analysis and the Taylor plot show that the RFR model is best suited for predicting daily garbage production in a city. This research, therefore, provides a Random Forest model that is optimal for such challenges and is recommended for this class of problem.