Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City

Nicholas E. Johnson, Olga Ianiuk, Daniel Cazap, Linglan Liu, Daniel Starobin, Gregory Dobler, Masoud Ghandehari

Research output: Contribution to journalArticlepeer-review

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 (R2>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.

Original languageEnglish (US)
Pages (from-to)3-11
Number of pages9
JournalWaste Management
Volume62
DOIs
StatePublished - Apr 2017

Keywords

  • Gradient boosting
  • New York City
  • Prediction
  • Waste

ASJC Scopus subject areas

  • Waste Management and Disposal

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