Predicting curbside food waste generation from responses to Self-Administered Surveys: An Application of Machine learning to improve household estimates.

Accurate measurement of household food waste is essential for designing and evaluating waste reduction and management policies. However, the mode of waste measurement and the approach to formulating sample populations whose waste is measured can impact accuracy. We find estimates of household food waste differ between measurement approaches applied to the same sample of participants and between the same measurement approach applied to differently recruited samples. The magnitudes of the estimates of per-person once-edible food waste follow the same order as the relative costs of collecting each type of data with the order from low to high being self-administered surveys (SAS) from a national consumer panel (388 g/person/week, $4.42/sample), SAS from locally recruited residents (552 g/person/week, $37.50/sample), and curbside waste composition analysis (WCA) from locally recruited residents (809 g/person/week, $375/sample). This study aims to bridge the gap across measurement and sampling approaches by using machine learning techniques to predict WCA-based edible and inedible food waste from SAS data. Using data from locally recruited respondents with contemporaneous SAS and WCA measurements, we develop least absolute shrinkage and selection operator (LASSO) regression models to predict WCA outcomes with data collected via SAS and compare these predictions against a literature based standard adjustment method. Our results show that LASSO models outperform the root mean square error of the standard adjustment method's predictions by 9 to 12 percent, hence providing improved ability to predict curbside waste amounts from low-cost survey data. Implications for cost-effective evaluation of policies affecting household food waste levels are discussed.