Assessing the Trustworthiness of Crowdsourced Rainfall Networks: A
Reputation System Approach
Abstract
High resolution and accurate rainfall information is essential to
modeling and predicting hydrological processes. Crowdsourced personal
weather stations (PWSs) have become increasingly popular in recent years
and can provide dense spatial and temporal resolution in rainfall
estimates. However, their usefulness is limited due to a lack of trust
in crowdsourced data compared to traditional data sources. Using
crowdsourced PWSs data without an evaluation of its trustworthiness can
result in inaccurate rainfall estimates as PWSs may be poorly maintained
or incorrectly sited. In this study, we advance the Reputation System
for Crowdsourced Rainfall Networks (RSCRN) to bridge this trust gap by
assigning dynamic trust scores to the PWSs. Using rainfall data
collected from 18 PWSs in two dense clusters in Houston, Texas USA as a
case study, the results show that using RSCRN-derived trust scores can
increase the accuracy of 15-min PWS rainfall estimates when compared to
rainfall observations recorded at city’s high-fidelity rainfall
stations. Overall, RSCRN rainfall estimates improved for 77% (48 out of
62) of the analyzed storm events, with a median RMSE improvement of
27.3%. Compared to an existing PWS quality control method, results
showed that while 13 (21%) storm events had the same performance, RSCRN
improved rainfall estimates for 78% of the remaining storm events (38
out of 49), with a median RMSE improvement of 13.4%. Using
RSCRN-derived trust scores can make the rapidly growing network of PWSs
a more useful resource for urban flood management, greatly improving
knowledge of rainfall patterns in areas with dense PWSs.