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A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir
A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir
Dissertations and Theses
Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …
An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza
An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza
Dissertations and Theses
Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …