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Prediction and Forecasting Municipal Solid Waste Generation of Northeastern Cities by Retrofitting ML Models of Guwahati, India

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Artificial Intelligence and Data Science Based R&D Interventions (NERC 2022)

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Abstract

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system require accurate estimation of future waste generation quantities. The main objective of this study was to develop models for accurate prediction and forecasting of municipal solid waste generation (MSWG) based on demographic and socio-economic variables. Machine learning (ML) models were learned by mapping residential MSW quantities with socio-economic and demographic parameters of Guwahati, Cachar and Imphal, Northeast India. Socio-economic variables were derived from Indian Census data and MOSPI at municipal levels and MSWG data from CPCB, Government of India. Three ML algorithms, namely decision tree (DT), random forest (RF) and gradient boosting (GB), were applied to build the models. Additionally, moving average (MA) approaches were adapted for forecasting the MSWG rate. Experimental results showed that ML algorithms can be efficaciously applied to learn waste models with good prediction performance. Further, GB was taking more computation time because of its high complexity. The approach demonstrated in this study is by gathering widely available data in the public domain, pre-processing, integrating, and modelling from various sources to produce tools that aid in waste planning and management.

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Abbreviations

MOSPI :

Ministry of Statistics and Programme Implementation

CPCB :

Central Pollution Control Board

MSWG :

Municipal Solid Waste Generation

SWM :

Solid waste management

MSW :

Municipal Solid Waste

SWG :

Solid Waste Generation

AI :

Artificial Intelligence

ANN :

Artificial Neural Network

SVM :

Support Vector Machine

LR :

Linear Regression

MLR :

Multiple Linear Regression

POP :

Population

LP :

Literate Population

WP :

Worker Population

HH :

Household

GDDP :

Gross District Domestic Product

ML :

Machine Learning

DT :

Decision Tree

RF :

Random Forest

GB :

Gradient Boosting

GBRT :

Gradient Boosting Regression Trees

MA :

Moving Average

SMA :

Simple Moving Average

WMA :

Weighted Moving Average

EMA :

Exponential Moving Average

HPO :

Hyper Parameter Optimization

RMSE :

Root Mean Squared Error

MSE :

Mean Squared Error

MAE :

Mean Absolute Error

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Authors and Affiliations

  1. Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Tinka Singh

  2. Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Ramagopal V. S. Uppaluri

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  1. Tinka Singh
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  2. Ramagopal V. S. Uppaluri
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Correspondence to Ramagopal V. S. Uppaluri .

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Editors and Affiliations

  1. Department of Electronics and Electrical Engineering and Head Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Ratnajit Bhattacharjee

  2. Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, India

    Debanga Raj Neog

  3. Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, India

    Konda Reddy Mopuri

  4. Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, India

    Santosh Kumar Vipparthi

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Singh, T., Uppaluri, R.V.S. (2023). Prediction and Forecasting Municipal Solid Waste Generation of Northeastern Cities by Retrofitting ML Models of Guwahati, India. In: Bhattacharjee, R., Neog, D.R., Mopuri, K.R., Vipparthi, S.K. (eds) Artificial Intelligence and Data Science Based R&D Interventions. NERC 2022. Springer, Singapore. https://doi.org/10.1007/978-981-99-2609-1_7

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