Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia
Citizen Science can fulfill the quest for high-quality and sufficient environmental data, such as rainfall. However, the factors affecting the quality of rainfall data collected by the citizen scientists are not well understood. In this study, we examined the effect of citizen scientists’ attributes...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2022
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| Acceso en línea: | https://hdl.handle.net/10568/126407 |
| _version_ | 1855531524397465600 |
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| author | Tedla, H. Z. Haile, Alemseged Tamiru Walker, D. W. Melesse, A. M. |
| author_browse | Haile, Alemseged Tamiru Melesse, A. M. Tedla, H. Z. Walker, D. W. |
| author_facet | Tedla, H. Z. Haile, Alemseged Tamiru Walker, D. W. Melesse, A. M. |
| author_sort | Tedla, H. Z. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Citizen Science can fulfill the quest for high-quality and sufficient environmental data, such as rainfall. However, the factors affecting the quality of rainfall data collected by the citizen scientists are not well understood. In this study, we examined the effect of citizen scientists’ attributes on the quality of rainfall data. For this purpose, Principal Component Analysis (PCA), stepwise regression and Multiple Linear Regressions (MLR) were used. A quality control procedure was developed and applied for daily observed rainfall data collected in the summer rainy season of 2020. Attributes of the citizen scientists’ were gathered for those who collected rainfall data in the urban and peri-urban Akaki catchment which is located in the Upper Awash sub-basin, Ethiopia. We found that easy-to-detect errors, which were identified during the initial stage of quality control, formed most of the errors in the rainfall data. The PCA and the stepwise regression results revealed that four dominant attributes (education level, gauge relative location, use of smartphone app, and supervisor’s travel distance) highly affected the rainfall data quality. The MLR model using these four prominent dominant variables performed very well with R2 value of 0.98. The k-fold cross validation result showed that the developed model can be used to predict the relationships between data quality and attributes of citizen scientists with high accuracy. Hence, the PCA technique, stepwise regression and MLR model can provide useful information regarding the influence of citizen scientists’ attributes on rainfall data quality. Therefore, future studies should carefully consider citizen scientists’ attributes when engaging and supervising citizen scientists, with a comprehensive data quality control while monitoring rainfall. |
| format | Journal Article |
| id | CGSpace126407 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1264072025-12-08T10:11:39Z Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia Tedla, H. Z. Haile, Alemseged Tamiru Walker, D. W. Melesse, A. M. citizen science rain weather data data quality catchment areas monitoring principal component analysis Citizen Science can fulfill the quest for high-quality and sufficient environmental data, such as rainfall. However, the factors affecting the quality of rainfall data collected by the citizen scientists are not well understood. In this study, we examined the effect of citizen scientists’ attributes on the quality of rainfall data. For this purpose, Principal Component Analysis (PCA), stepwise regression and Multiple Linear Regressions (MLR) were used. A quality control procedure was developed and applied for daily observed rainfall data collected in the summer rainy season of 2020. Attributes of the citizen scientists’ were gathered for those who collected rainfall data in the urban and peri-urban Akaki catchment which is located in the Upper Awash sub-basin, Ethiopia. We found that easy-to-detect errors, which were identified during the initial stage of quality control, formed most of the errors in the rainfall data. The PCA and the stepwise regression results revealed that four dominant attributes (education level, gauge relative location, use of smartphone app, and supervisor’s travel distance) highly affected the rainfall data quality. The MLR model using these four prominent dominant variables performed very well with R2 value of 0.98. The k-fold cross validation result showed that the developed model can be used to predict the relationships between data quality and attributes of citizen scientists with high accuracy. Hence, the PCA technique, stepwise regression and MLR model can provide useful information regarding the influence of citizen scientists’ attributes on rainfall data quality. Therefore, future studies should carefully consider citizen scientists’ attributes when engaging and supervising citizen scientists, with a comprehensive data quality control while monitoring rainfall. 2022-09 2022-12-31T23:05:05Z 2022-12-31T23:05:05Z Journal Article https://hdl.handle.net/10568/126407 en Limited Access Elsevier Tedla, H. Z.; Haile, Alemseged Tamiru; Walker, D. W.; Melesse, A. M. 2022. Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia. Journal of Hydrology, 612(Part C):128284. [doi: https://doi.org/10.1016/j.jhydrol.2022.128284] |
| spellingShingle | citizen science rain weather data data quality catchment areas monitoring principal component analysis Tedla, H. Z. Haile, Alemseged Tamiru Walker, D. W. Melesse, A. M. Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia |
| title | Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia |
| title_full | Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia |
| title_fullStr | Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia |
| title_full_unstemmed | Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia |
| title_short | Evaluation of factors affecting the quality of citizen science rainfall data in Akaki Catchment, Addis Ababa, Ethiopia |
| title_sort | evaluation of factors affecting the quality of citizen science rainfall data in akaki catchment addis ababa ethiopia |
| topic | citizen science rain weather data data quality catchment areas monitoring principal component analysis |
| url | https://hdl.handle.net/10568/126407 |
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