Development of Municipal Solid Waste Generation and Expenditures: Case of Czech Municipalities

2016 ◽  
pp. 399-416
Author(s):  
Michal Struk
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Waste Generation ◽  
Solid Waste Generation

Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches

2018 ◽  
Vol 74 ◽  
pp. 3-15 ◽  
Author(s):  
Miyuru Kannangara ◽  
Rahul Dua ◽  
Leila Ahmadi ◽  
Farid Bensebaa
Keyword(s):  
Machine Learning ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Learning Approaches ◽  
Waste Generation ◽  
Modeling And Prediction ◽  
Solid Waste Generation

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

Impact of socioeconomic status on municipal solid waste generation rate

2016 ◽  
Vol 49 ◽  
pp. 15-25 ◽  
Author(s):  
D. Khan ◽  
A. Kumar ◽  
S.R. Samadder
Keyword(s):  
Socioeconomic Status ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Generation Rate ◽  
Waste Generation ◽  
Solid Waste Generation ◽  
Waste Generation Rate

Assessment of municipal solid waste generation and recyclable materials potential in Kuala Lumpur, Malaysia

2009 ◽  
Vol 29 (7) ◽  
pp. 2209-2213 ◽  
Author(s):  
Mohamed Osman Saeed ◽  
Mohd Nasir Hassan ◽  
M. Abdul Mujeebu
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Waste Generation ◽  
Kuala Lumpur ◽  
Solid Waste Generation

Estimating municipal solid waste generation by different activities and various resident groups: A case study of Beijing

2011 ◽  
Vol 409 (20) ◽  
pp. 4406-4414 ◽  
Author(s):  
Zhen-shan Li ◽  
Hui-zhen Fu ◽  
Xiao-yan Qu
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Waste Generation ◽  
Solid Waste Generation
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