scholarly journals Application of Multi Linear Model for Forecasting Municipal Solid Waste Generation in Lucknow City: A Case Study

2019 ◽  
Vol 14 (3) ◽  
pp. 421-432
Author(s):  
Apoorv Verma ◽  
Alok Kumar ◽  
N. B. Singh
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Management System ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Waste Generation ◽  
System Failures ◽  
Msw Management ◽  
Solid Waste Generation ◽  
Socio Economic Variables

The objective of this study is to forecast the Municipal Solid Waste (MSW) quantity output in Lucknow city by 2025, establishing a relationship between various socio-economic variables and waste generated using Multiple Linear Regression Analysis (MLRA). It is found that the rate of generation of MSW in Lucknow increases after 1383 M ton d-1 in the year 2015 to 2075 M ton d-1 in the year 2025 and per capita waste increases after 0.465 kg capita-1 day-1 in 2015 to 0.616 kg capita-1 day-1 in 2025 respectively. The outcomes of the research are reliable to ascertain waste generation quantities in future, a pool of factors pivotal in the prevalent composition of the waste and a feasible way ahead towards a proper MSW management system as per the varying composition of solid wastes. The statistics provided in this paper is very useful for proper arrangement and executing the best waste management system in Lucknow City to avoid system failures.

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

2021 ◽  
Vol 6 (5) ◽  
pp. 1276-1296
Author(s):  
Mohd Anjum ◽  
Sana Shahab ◽  
Mohammad Sarosh Umar
Keyword(s):  
Linear Regression ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Likelihood Estimation ◽  
Grey Model ◽  
Waste Generation ◽  
Elementary Model ◽  
Grey Forecasting ◽  
Solid Waste Generation ◽  
Forecasting Theory

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.

Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting

2018 ◽  
Vol 20 (3) ◽  
pp. 1761-1770 ◽  
Author(s):  
Leaksmy Chhay ◽  
Md Amjad Hossain Reyad ◽  
Rathny Suy ◽  
Md Rafiqul Islam ◽  
Md Manik Mian
Keyword(s):  
Factor Analysis ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Influencing Factor ◽  
Waste Generation ◽  
Solid Waste Generation
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