Artificial intelligence-based biomonitoring of water quality
The miniSASS was developed as a citizen science tool for monitoring the health of river systems and reflecting the water quality through assessing macroinvertebrates communities. The miniSASS samples the macroinvertebrate community in a river reach and compares the community present to the expected...
| Autores principales: | , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
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CGIAR Initiative on Digital Innovation
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/128025 |
| _version_ | 1855541425530208256 |
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| author | Pattinson, N. B. Kuen, R. |
| author_browse | Kuen, R. Pattinson, N. B. |
| author_facet | Pattinson, N. B. Kuen, R. |
| author_sort | Pattinson, N. B. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The miniSASS was developed as a citizen science tool for monitoring the health of river systems and reflecting the water quality through assessing macroinvertebrates communities. The miniSASS samples the macroinvertebrate community in a river reach and compares the community present to the expected community under ideal natural conditions. The information garnered during a survey relies heavily on the accurate identification of macroinvertebrates by lows killed citizen scientists. This leaves a potential for errors in identification which may impact the accuracy of results and, ultimately, of the river health assessment. In response, we initiated the development of a smartphone application with built-in machine-learning algorithms for the automatic, real-time identification of macroinvertebrates. This report presents our data, methodology, and preliminary results from the automated identification algorithms. |
| format | Informe técnico |
| id | CGSpace128025 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | CGIAR Initiative on Digital Innovation |
| publisherStr | CGIAR Initiative on Digital Innovation |
| record_format | dspace |
| spelling | CGSpace1280252025-11-07T09:03:50Z Artificial intelligence-based biomonitoring of water quality Pattinson, N. B. Kuen, R. water quality biomonitoring artificial intelligence rivers citizen science macroinvertebrates machine learning The miniSASS was developed as a citizen science tool for monitoring the health of river systems and reflecting the water quality through assessing macroinvertebrates communities. The miniSASS samples the macroinvertebrate community in a river reach and compares the community present to the expected community under ideal natural conditions. The information garnered during a survey relies heavily on the accurate identification of macroinvertebrates by lows killed citizen scientists. This leaves a potential for errors in identification which may impact the accuracy of results and, ultimately, of the river health assessment. In response, we initiated the development of a smartphone application with built-in machine-learning algorithms for the automatic, real-time identification of macroinvertebrates. This report presents our data, methodology, and preliminary results from the automated identification algorithms. 2022-12-08 2023-01-24T11:58:49Z 2023-01-24T11:58:49Z Report https://hdl.handle.net/10568/128025 en Open Access application/pdf CGIAR Initiative on Digital Innovation Pattinson, N. B.; Kuen, R.; Kuen, R. 2022. Artificial intelligence-based biomonitoring of water quality. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 32p. |
| spellingShingle | water quality biomonitoring artificial intelligence rivers citizen science macroinvertebrates machine learning Pattinson, N. B. Kuen, R. Artificial intelligence-based biomonitoring of water quality |
| title | Artificial intelligence-based biomonitoring of water quality |
| title_full | Artificial intelligence-based biomonitoring of water quality |
| title_fullStr | Artificial intelligence-based biomonitoring of water quality |
| title_full_unstemmed | Artificial intelligence-based biomonitoring of water quality |
| title_short | Artificial intelligence-based biomonitoring of water quality |
| title_sort | artificial intelligence based biomonitoring of water quality |
| topic | water quality biomonitoring artificial intelligence rivers citizen science macroinvertebrates machine learning |
| url | https://hdl.handle.net/10568/128025 |
| work_keys_str_mv | AT pattinsonnb artificialintelligencebasedbiomonitoringofwaterquality AT kuenr artificialintelligencebasedbiomonitoringofwaterquality |