USE OF MACHINE LEARNING IN PREDICTING THE GENERATION OF SOLID WASTE

Authors

  • Tushar Rathod, Manoj Hudnurkar, Suhas Ambekar

Abstract

This study is focussed on the betterment of municipal solid waste management and it has
an intention to build machine learning models so that we municipalities can predict solid waste
generation with the help of demographic and socio-economic variables. These machine learning
models are built by placing domestic municipal solid waste quantities together with demographic
and socio-economic variables of 200 regions around the area of Akola city, Maharashtra. We are
using four machine learning algorithms viz., decision tree, multiple linear regression, random
forest regression, and XGBoost regression algorithms to build predictive models. We needed the
data on solid waste generated in those regions and it has been collected from local authorities and
the website of Akola Municipal Corporation. From Indian Census and local authorities, we got
data for demographic and socio-economic variables. The data pre-processing required in the
initial stages of the model building was performed in Python and Microsoft Excel. After this, the
models were built with machine learning algorithms and the results of this study showed that
these algorithms can be used to estimate solid waste generated in the areas depending on the
variables with a good prediction performance. The random forest regression model has the best
performance, describing the minimum difference between actual and predicted values of solid
waste generated. In this study, we have following the approach where we demonstrate the
feasibility of building machine learning models that help in regional waste planning using data
collection, data cleaning, data pre-processing, and modelling of available data.

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Published

2020-12-01

How to Cite

Tushar Rathod, Manoj Hudnurkar, Suhas Ambekar. (2020). USE OF MACHINE LEARNING IN PREDICTING THE GENERATION OF SOLID WASTE. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(6), 4323 - 4335. Retrieved from http://mail.palarch.nl/index.php/jae/article/view/1687