Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals
Due to lack of understanding about the different conditions of sanitation systems and complexity, mostly in Asia and Africa, the occurrence of unsafely managed sanitation still exists, leading to severe environmental and health impacts. Because of the complexity of this problem, machine learning too...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Environmental and Sustainability Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972724000424 |
| _version_ | 1797268962468167680 |
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| author | Achara Taweesan Thammarat Koottatep Thongchai Kanabkaew Chongrak Polprasert |
| author_facet | Achara Taweesan Thammarat Koottatep Thongchai Kanabkaew Chongrak Polprasert |
| author_sort | Achara Taweesan |
| collection | DOAJ |
| description | Due to lack of understanding about the different conditions of sanitation systems and complexity, mostly in Asia and Africa, the occurrence of unsafely managed sanitation still exists, leading to severe environmental and health impacts. Because of the complexity of this problem, machine learning tools were applied to develop an effective model for health protection and safe sanitation management as a promising way to approach sustainable development goals. This study aimed to examine the incidences of ineffective sanitation management on the prevalence of diarrhea infections using machine learning tools. The prevailing conditions for safely sanitation management were identified, and the effective model to protect the impacts of ineffective sanitation management on the prevalence of diarrhea infections was proposed. Based on information collected from about 1000 households with relatively high diarrhea infections during the period of 2017–2021, factors relating to sanitation facilities for health protection and safe sanitation management were examined. Diarrhea infections and no diarrhea infection were recognized based on actual conditions of the surveyed households and incidences from ground-based observation. Classification tree model as J48 in WEKA was applied for analytic predictive tool using 70:30 ratio of training and validating dataset. The findings showed that the tree model obtained from the training data was with 73% accuracy prediction, while that from the validation data was with 70% accuracy. The correlation of personal hygiene (such as washing hand before meal and drinking water from natural water sources) and sanitation facilities (such as open defecation and distance from on-site sanitation facilities to open drains or storm sewer) was significant with inverse relationship for safely sanitation management and public health protection. |
| first_indexed | 2024-04-25T01:40:49Z |
| format | Article |
| id | doaj.art-2a454e2a495147f182314cadceb26f0f |
| institution | Directory Open Access Journal |
| issn | 2665-9727 |
| language | English |
| last_indexed | 2024-04-25T01:40:49Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj.art-2a454e2a495147f182314cadceb26f0f2024-03-08T05:19:20ZengElsevierEnvironmental and Sustainability Indicators2665-97272024-06-0122100374Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goalsAchara Taweesan0Thammarat Koottatep1Thongchai Kanabkaew2Chongrak Polprasert3Department of Environmental Science, Faculty of Science, Ramkhamhaeng University, Bangkok, ThailandSchool of Environment, Resources and Development, Asian Institute of Technology, Pathumthani, ThailandOccupational and Environmental Health Program, Faculty of Public Health, Thammasat University, Pathumthani, Thailand; Corresponding author.Department of Civil Engineering, Faculty of Engineering, Thammasat University, Pathumthani, ThailandDue to lack of understanding about the different conditions of sanitation systems and complexity, mostly in Asia and Africa, the occurrence of unsafely managed sanitation still exists, leading to severe environmental and health impacts. Because of the complexity of this problem, machine learning tools were applied to develop an effective model for health protection and safe sanitation management as a promising way to approach sustainable development goals. This study aimed to examine the incidences of ineffective sanitation management on the prevalence of diarrhea infections using machine learning tools. The prevailing conditions for safely sanitation management were identified, and the effective model to protect the impacts of ineffective sanitation management on the prevalence of diarrhea infections was proposed. Based on information collected from about 1000 households with relatively high diarrhea infections during the period of 2017–2021, factors relating to sanitation facilities for health protection and safe sanitation management were examined. Diarrhea infections and no diarrhea infection were recognized based on actual conditions of the surveyed households and incidences from ground-based observation. Classification tree model as J48 in WEKA was applied for analytic predictive tool using 70:30 ratio of training and validating dataset. The findings showed that the tree model obtained from the training data was with 73% accuracy prediction, while that from the validation data was with 70% accuracy. The correlation of personal hygiene (such as washing hand before meal and drinking water from natural water sources) and sanitation facilities (such as open defecation and distance from on-site sanitation facilities to open drains or storm sewer) was significant with inverse relationship for safely sanitation management and public health protection.http://www.sciencedirect.com/science/article/pii/S2665972724000424Faecal pathogen infectionsMachine learningSanitation effectiveness modelSustainable development goals |
| spellingShingle | Achara Taweesan Thammarat Koottatep Thongchai Kanabkaew Chongrak Polprasert Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals Environmental and Sustainability Indicators Faecal pathogen infections Machine learning Sanitation effectiveness model Sustainable development goals |
| title | Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals |
| title_full | Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals |
| title_fullStr | Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals |
| title_full_unstemmed | Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals |
| title_short | Application of machine learning in sanitation management prediction: Approaches for achieving sustainable development goals |
| title_sort | application of machine learning in sanitation management prediction approaches for achieving sustainable development goals |
| topic | Faecal pathogen infections Machine learning Sanitation effectiveness model Sustainable development goals |
| url | http://www.sciencedirect.com/science/article/pii/S2665972724000424 |
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