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Decision making in utilities, municipal, and energy companies depends on accurate and trustworthy weather information and predictions. Recently, crowdsourced personal weather stations (PWS) are being widely used to provide a higher spatial and temporal resolution of weather measurements. For instance, increasing attention is being paid to the potential of PWS data to improve flash-flood assessment and prediction. However, tools and methods to ensure the trustworthiness of the crowd-sourced data in real-time are largely missing. In this paper, we present a Reputation System for Crowdsourced Rainfall Networks (RSCRN) to assign trust scores to personal weather stations in a region. Using real PWS data from the Weather Underground service in the high flood risk region of Norfolk, Virginia, we validate the performance and robustness of the proposed RSCRN. The proposed method is able to converge to a confident trust score for a PWS within 10-20 observations of installation and can robustly respond to any discrepancies in the data due to failure or malicious intent. We benchmark the performance of the proposed method with high-fidelity and trusted hydrological sensor data, which are usually expensive to install. Collectively, the results indicate that the trust score derived from the RSCRN can reflect the collective measure of trustworthiness to the PWS, ensuring both useful and trustworthy data for modeling and decision-making in the future.

Reputation system for ensuring data trustworthiness of crowdsourced weather stations