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.

Citizen Science 可以满足对降雨等高质量和充足环境数据的需求。然而，影响公民科学家收集的降雨数据质量的因素尚不清楚。在这项研究中，我们检验了公民科学家的属性对降雨数据质量的影响。为此，使用了主成分分析 (PCA)、逐步回归和多元线性回归 (MLR)。为 2020 年夏季雨季收集的每日观测降雨数据开发并应用了质量控制程序。收集了收集城市和周边降雨数据的公​​民科学家的属性- 位于埃塞俄比亚上阿瓦什次流域的城市 Akaki 集水区。我们发现，在质量控制的初始阶段发现的易于检测的错误构成了降雨数据中的大部分错误。PCA 和逐步回归结果表明，四个主要属性（教育水平、仪表相对位置、智能手机应用程序的使用和主管的行进距离）对降雨数据质量有很大影响。使用这四个显着的主导变量的 MLR 模型在 R ^{2下表现得非常好}值为 0.98。k折交叉验证结果表明，所开发的模型可以高精度地预测数据质量与公民科学家属性之间的关系。因此，PCA 技术、逐步回归和 MLR 模型可以提供有关公民科学家属性对降雨数据质量影响的有用信息。因此，未来的研究在吸引和监督公民科学家时应仔细考虑公民科学家的属性，在监测降雨的同时进行全面的数据质量控制。