scholarly journals IMPACT OF GROSS DOMESTIC PRODUCT CHANGE ON MUNICIPAL SOLID WASTE GENERATION IN MAPUTO, MOZAMBIQUE

2020 ◽  
Author(s):  
AMAD H. A. GANI ◽  
ANTÓNIO G. DIAS ◽  
ANTÓNIO A. R. MONJANE
Keyword(s):  
Gross Domestic Product ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Domestic Product ◽  
Waste Generation ◽  
Solid Waste Generation

scholarly journals The solid waste in Colombia analyzed via gross domestic product: towards a sustainable economy

2020 ◽  
pp. 51-63
Author(s):  
Henry Alonso Colorado-Lopera ◽  
Gloria Inés Echeverri-Lopera
Keyword(s):  
Gross Domestic Product ◽  
Solid Waste ◽  
Latin American ◽  
Construction And Demolition Waste ◽  
Domestic Product ◽  
Waste Generation ◽  
Demolition Waste ◽  
Cultural Aspects ◽  
Sustainable Economy ◽  
Solid Waste Generation

The main goal in this research is to study the Colombia’s solid waste in relation to the general trends of the gross domestic product of the country, a more general overview of the situation with respect to other neighbor countries and some leading economies. The method followed was the analysis and processing of the official and unofficial data of the country, for constructing useful information such as the gross domestic product (GDP), discussed in relation to the generated waste. Since waste related issues demand and requires multi-disciplinary solutions, legal and cultural aspects are also considered in the discussion. The main contribution of this research is new, analized and consolidated data regarding the current economic model in Colombia towards the implementation of a sustainable economy, presented with respect to Latin American and to some leading world economies as well. The investigation has been focused on the sectors that are less-known with respect to the solid waste generation, particularly to the GDP of the construction and demolition waste, and mining wastes, which are of great interest in Colombia for its type of industry.

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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