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Machine learning tool-based prediction and forecasting of municipal solid waste generation rate: a case study in Guwahati, Assam, India

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Abstract

Integrated large-scale solid waste management (SWM) policies are the need of the hour to design, develop and sustain SWM models. An accurate prediction and forecasting of municipal solid waste generation (MSWG) rate are essential for such advanced strategies. The primary objective of this study is to examine the criticality of demographic and socio-economic parameters for the fair prediction and forecasting of the MSWG rate. Machine learning (ML) models were formulated by mapping solid waste quantities at the municipal level with socio-economic and demographic variables of Guwahati city. Tree-based ML algorithms, namely decision tree (DT), random forest (RF) and gradient boosting (GB), were applied to build the models with 1936 data size. The moving average (MA) approaches were adapted for the forecasting of the MSWG rate. Model validation resulted in a root mean square error, RMSE (3.01), mean absolute error, MAE (2.86) and coefficient of determination, R2 (0.99) for the GB model and correlation coefficient (r) of 0.82 between observed and predicted values and thereby resulted in best performance in conjunction with DT and RF. With the exponential MA, the forecasted RMSE and R2 for GB, RF and DT were 2.12, 3.63 and 4.22; and 0.981, 0.972 and 0.967, respectively. However, with a model accuracy of 97%, the computation time for GB model (19.18 min) exhibited maximum due to its high complexity. The overall methodology involved developing effective tools to aid in regional SWM and planning through the integration of data sources in the public domain, pre-processing and modelling from diverse sources.

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Abbreviations

R 2 DT :

Coefficient of determination for decision tree

RMSEDT :

Root mean square error for decision tree model

R 2 RF :

Coefficient of determination for random forest

RMSERF :

Root mean square error for random forest model

R 2 GB :

Coefficient of determination for gradient boosting

RMSEGB :

Root mean square error for gradient boosting model

EMADT :

Exponential moving average for decision tree

SMADT :

Simple moving average for decision tree

WMADT :

Weighted moving average for decision tree

EMARF :

Exponential moving average for random forest

SMARF :

Simple moving average for random forest

WMARF :

Weighted moving average for random forest

EMAGB :

Exponential moving average for gradient boosting

SMAGB :

Simple moving average for gradient boosting

WMAGB :

Weighted moving average for gradient boosting

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Acknowledgements

The authors thankfully acknowledge the Centre for the Environment, Indian Institute of Technology Guwahati, India, Guwahati Municipal Corporation (GMC), Assam, India, Office of the Registrar General & Census Commissioner, India (ORGI), Central Pollution Control Board (CPCB) and Ministry of Statistics Programme and Implementation (MOSPI) for providing requisite data for carrying out this research.

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  1. Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    T. Singh

  2. Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati,, 781039, India

    R. V. S. Uppaluri

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TS was involved in data extraction, data pre-processing, investigation, modelling, writing the original draft, writing—reviewing and editing, and validation. Ramagopal VSU was responsible for conceptualization, supervision, validation, visualization, and writing—reviewing and editing.

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Correspondence to R. V. S. Uppaluri.

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Singh, T., Uppaluri, R.V.S. Machine learning tool-based prediction and forecasting of municipal solid waste generation rate: a case study in Guwahati, Assam, India. Int. J. Environ. Sci. Technol. 20, 12207–12230 (2023). https://doi.org/10.1007/s13762-022-04644-4

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