Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City. (April 2017)

• Record Type: Journal Article
• Authors: Johnson, Nicholas E.; Ianiuk, Olga; Cazap, Daniel; Liu, Linglan; Starobin, Daniel; Dobler, Gregory; Ghandehari, Masoud
• Is Part Of: Waste management. Volume 62(2017)
• Page Start: 3
• Page End: 11
• Publication Date: 2017-04
• Subjects: Waste -- Gradient boosting -- Prediction -- New York City; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2017.01.037
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: Waste generation in dense urban areas exhibit regular high and low frequency patterns. A gradient boosting regression model is built to predict weekly waste generation. The model uses ten years of high-resolution historical refuse and recycling data. Weather and historical data are the most significant features for prediction. Prediction accuracy varies per waste type; refuse prediction shows best performance. Abstract: Historical municipal solid waste (MSW) collection data supplied by the New York City Department of Sanitation (DSNY) was used in conjunction with other datasets related to New York City to forecast municipal solid waste generation across the city. Spatiotemporal tonnage data from the DSNY was combined with external data sets, including the Longitudinal Employer Household Dynamics data, the American Community Survey, the New York City Department of Finance's Primary Land Use and Tax Lot Output data, and historical weather data to build a Gradient Boosting Regression Model. The model was trained on historical data from 2005 to 2011 and validation was performed both temporally and spatially. With this model, we are able to accurately ( R 2 > 0.88 ) forecast weekly MSW generation tonnages for each of the 232 geographic sections in NYC across three waste streams of refuse, paper and metal/glass/plastic. Importantly, the model identifies regularity of urban waste generation and is also able to capture very short timescale fluctuations associated to holidays, special events, seasonal variations, and weather related events. This research shows New York City's waste generation trends and the importance of comprehensive data collection (especially weather patterns) in order to accurately predict waste generation.