Predictive models using "cheap and easy" field measurements: Can they fill a gap in planning, monitoring, and implementing fecal sludge management solutions?
Random forest
WASH
fecal sludge
image analysis
machine learning
sanitation
Journal
Water research
ISSN: 1879-2448
Titre abrégé: Water Res
Pays: England
ID NLM: 0105072
Informations de publication
Date de publication:
15 May 2021
15 May 2021
Historique:
received:
10
11
2020
revised:
19
02
2021
accepted:
01
03
2021
pubmed:
22
3
2021
medline:
14
4
2021
entrez:
21
3
2021
Statut:
ppublish
Résumé
The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH
Identifiants
pubmed: 33744658
pii: S0043-1354(21)00195-0
doi: 10.1016/j.watres.2021.116997
pii:
doi:
Substances chimiques
Sewage
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
116997Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.