Applying Grey System Theory for MSW Forecasting in Data-Limited Urban Environments: A Case Study of Shillong, Meghalaya
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Authors
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Nidahamteinsar Papang
North Eastern Hill University, Shillong
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Mebalarisha Kharkongor
North Eastern Hill University, Shillong
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Briefully Kharnongrum
North Eastern Hill University, Shillong
Keywords:
Municipal Solid Waste; Grey System Theory; GM(1,1); Forecasting; Shillong; Waste Management.Abstract
Rapid urbanization, population growth, and shifting consumption patterns have made municipal solid waste (MSW) management a pressing
environmental issue. Accurate projections of waste generation are critical for planning collection, transportation, treatment, and disposal systems.
However, conventional forecasting methods typically require large datasets, which are often unavailable in developing cities.
This study applies the Grey System Theory GM(1,1) model to forecast MSW generation in Shillong, Meghalaya, using annual data from the Shillong
Municipal Board for 2021–2025. GM(1,1) was chosen for its capacity to produce reliable forecasts with limited data. Parameters were estimated using
the least squares method, and model performance was assessed using MAE, MAPE, and RMSE.
The model demonstrated strong accuracy with a MAPE of 1.28%, MAE of 1123.76 MT, and RMSE of 1096.66 MT. Forecasts indicate that annual
MSW generation in Shillong will rise from about 75,998 MT in 2026 to 98,227 MT in 2035, an increase from 208 TPD to 269 TPD. This upward
trend underscores the need for upgraded waste management infrastructure and strategic long-term planning.
The findings confirm that GM(1,1) is an effective forecasting tool for data-scarce urban settings and offers practical insights for sustainable waste
management in Shillong
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