Application of Modified Grey Forecasting Model to Predict the Municipal Solid Waste Generation using MLP and MLE

Abstract

Grey forecasting theory is an approach to build a prediction model with limited data to produce better forecasting results. This forecasting theory has an elementary model, represented as the GM(1,1) model , characterized by the first-order differential equation of one variable. It has the potential for accurate and reliable forecasting without any statistical assumption. The research proposes a methodology to derive the modified GM(1,1) model with improved forecasting precision. The residual series is forecasted by the GM(1,1) model to modify the actual forecasted values. The study primarily addresses two fundamental issues: sign prediction of forecasted residual and the procedure for formulating the grey model. Accurate sign prediction is very complex, especially when the model lacks in data. The signs of forecasted residuals are determined using a multilayer perceptron to overcome this drawback. Generally, the elementary model is formulated conventionally, containing the parameters that cannot be calculated straightforward. Therefore, maximum likelihood estimation is incorporated in the modified model to resolve this drawback. Three statistical indicators, relative residual, posterior variance test, and absolute degree of grey indices, are evaluated to determine the model fitness and validation. Finally, an empirical study is performed using actual municipal solid waste generation data in Saudi Arabia, and forecasting accuracies are compared with the linear regression and original GM(1,1). The MAPEs of all models are rigorously examined and compared, and then it is obtained that the forecasting precision of GM(1,1) model , modified GM(1,1) model, and linear regression is 15.97%, 8.90%, and 27.90%, respectively. The experimental outcomes substantiate that the modified grey model is a more suitable forecasting approach than the other compared models.

Grey Model Analysis of Vehicle Population, Road Transport Energy Consumption and Vehicular Emissions

“…Since then, many variations of the model have been developed to improve its accuracy. The model has been used to investigate the CO 2 emissions in Asia-Pacific Economic Cooperation (APEC) member countries [29], to forecast route passenger demand in the air transport industry, to project natural gas consumption [30], to forecast municipal waste generation [31], to investigate biofuel production and consumption in top CO 2 emitting countries [32], to forecast electricity consumption [33][34] among many other applications in several fields. The model has found wide applications because of its simplicity, low data requirement and high prediction accuracy [35][36].…”

Application of Event Detection to Improve Waste Management Services in Developing Countries

“…In low-income countries, the total amount of waste generation will increase by three or more folds by 2050 [4]. The yield of MSW is tremendously growing in the urban regions and metro cities of developing countries [6]. If these residues are not handled through appropriate management, they can damage the environment and human and animal health [7].…”