Interpretable spatial machine learning insights into urban sanitation challenges: A case study of human feces distribution in San Francisco-Reference-Cited by-同舟云学术

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Interpretable spatial machine learning insights into urban sanitation challenges: A case study of human feces distribution in San Francisco

Published:2024-10 Issue: Volume:113 Page:105695
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Yi Shengao^ORCID,Li Xiaojiang,Wang Ruoyu,Guo Ziyi,Dong Xinyu,Liu Yixuan,Xu Qian^ORCID

Publisher

Elsevier BV

Reference101 articles.

1. Sustainable mass transit: Challenges and opportunities in urban public transportation;Abdallah,2023

2. Exploring the determinants of open defecation in Nigeria using demographic and health survey data;Abubakar;Science of the Total Environment,2018

3. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI);Adadi;IEEE Access,2018

4. Somewhere to go: assessing the impact of public restroom interventions on reports of open defecation in San Francisco, California from 2014 to 2020;Amato;BMC Public Health,2022

5. Towards “sustainable” sanitation: challenges and opportunities in urban areas;Andersson;Sustainability,2016

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