Various Models Used in Analysing Municipal Solid Waste Generation–A Review

2021 ◽  
Vol 47 (3) ◽  
pp. 569-578
Author(s):  
Rashmi Srinivasaiah ◽  
Devappa Renuka Swamy ◽  
Aswin S. Krishna ◽  
Chandrashekar Vinayak Airsang ◽  
Dinesh C. Reddy ◽  
...  
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Fuzzy Inference ◽  
Sample Survey ◽  
Waste Generation ◽  
Linear Regression Models ◽  
Inference System ◽  
Artificial Intelligence Models ◽  
Solid Waste Generation ◽  
Conventional Models

At present, factors such as growth in population, economic development, urbanization and improved standard of living increase the quantity and complexity of generated Municipal Solid Waste. The different approaches for developing models for forecasting municipal solid waste generation have been classified into conventional and non-conventional or artificial intelligence models. While the conventional models include sample survey, system dynamics, econometric models, time series analysis, factor driven models and multiple linear regression models, the non-conventional models include artificial neural networks, Fuzzy logic models and Adaptive Neuro Fuzzy Inference System models. In this review, various factors considered for modelling, locations of study, sources of data and various studies conducted by researchers have been tabulated in detail for identifying the major factors and models used in developed and developing countries. Non-conventional models are being preferred because of their capacity to analyse dynamic data and for their prediction accuracy.

scholarly journals Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

2017 ◽  
Vol 19 (3) ◽  
pp. 511-520 ◽  
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Temporal Scale ◽  
Age Group ◽  
Consumer Expenditure ◽  
Waste Generation ◽  
Inference System ◽  
Input Variables

Fuzzy Inference System (FIS) based prediction models for the Municipal Solid Waste (MSW) generation has been developed in the present work to study the influences of total population, percapita annual income, literacy rate, age group and monthly consumer expenditure on temporal variability of MSW generation for Kolhapur city, India. Ten models were developed considering two input variables at a time to study the effect of the socioeconomic and demographic parameters on MSW generation. Finally, all five input variables were considered in a single model to predict MSW generation in a temporal scale. Result shows that, the model with input variables consumer expenditure and age group was best fitted with highest coefficient of determination (0.985) value and lowest standard error of the estimate (1.562) value for the modelling period. For the design period, models related to consumer expenditure show higher waste generation. Models related to population and age show prediction similar to ‘Kolhapur Municipal Corporations’ prediction. However model with input literacy and income shows very low waste generation prediction. The proposed modelling technique is very useful in MSW generation prediction for a temporal scale in uncertain and random environment globally.

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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