Valuation of Sustainable Practices for Solid Waste ManagementThrough Time-series Forecasting Model using ARIMA
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Keywords
Solid waste management (SWM), Municipal solid waste (MSW), ARIMA forecasting model, Material recovery facility (MRF), Urban sustainability
Abstract
The present research investigates the integration and assessment of sustainable practices in the field of solid waste management (SWM) in Chandigarh, India, by focusing on enhancing the efficiency and sustainability of municipal waste operations through time-series forecasting. Chandigarh, an urban center, faces increasing challenges because of population burden and ever-increasing waste generation. Data-driven approaches to predict and manage municipal solid waste have become essential. In the present work, historical daily data collected from the Material Recovery Facility (a waste processing plant), Sector-25, Chandigarh, India, are utilized to develop forecasting models using the Autoregressive Integrated Moving Average (ARIMA) methodology. The present work focuses not only on forecasting the total waste received of a particular waste stream, viz. cardboard and thermocol, but also on some other specific waste categories, such as recyclable plastics, metals, glass, and coconut shells. The results revealed key trends, including a steady rise in waste quantities across most categories, seasonal fluctuations, and temporary anomalies potentially influenced by public events or climatic changes. The results revealed that integrating ARIMA-based forecasting into the Chandigarh Smart City dashboard fundamentally changes municipal waste management from a reactive struggle into a proactive strategy, which emphasizes the importance of predictive planning in SWM. Optimizing collection processes, upgrading sorting technologies at material recovery facilities, strengthening community participation in source segregation, and integrating real-time digital dashboards for municipal oversight are few recommendations. By bridging gap between academic modeling and municipal needs, this study offers a scalable framework for predictive and sustainable waste management practices in Chandigarh, India. This study contributes to the broader discourse on Chandigarh smart city infrastructure by offering useful insights for planners and urban policy makers striving to build resilient and environmentally sustainable waste systems.
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