Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities

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Municipal solid waste management represents an increasingly significant environmental, fiscal, and social challenge for cities. Understanding patterns of municipal waste generation behavior at the household and building scales is a critical component of efficient collection routing and the design of incentives to encourage recycling and composting. However, high spatial resolution estimates of building refuse and recycling have been constrained by the lack of granular data for individual properties. This paper presents a new analytical approach, which combines machine learning and small area estimation techniques, to predict weekly and daily waste generation at the building scale. Using daily collection data from 609 New York City Department of Sanitation (DSNY) sub-sections over ten years, together with detailed data on individual building attributes, neighborhood socioeconomic characteristics, weather, and selected route-level collection data, we apply gradient boosting regression trees and neural network models to estimate daily and weekly refuse and recycling tonnages for each of the more than 750,000 residential properties in the City. Following cross-validation and a two-stage spatial validation, our results indicate that our method is capable of predicting building-level waste generation with a high degree of accuracy. Our methodology has the potential to support collection truck route optimization based on expected building-level waste generation rates, and to facilitate new equitable solid waste management policies to shift behavior and divert waste from landfills based on benchmarking and peer performance comparisons.