Bridging the Trust Gap in Crowdsourced Hydrological Sensor Networks: Data Trustworthiness of Personal Weather Stations

Flooding is becoming more common in cities and communities worldwide, causing severe damage and loss of property. Assessing these flood risks and predicting floods rely on a wealth of accurate and trustworthy hydrological information. Recently, increasing attention is being paid to the potential of crowdsourced rainfall networks, with large number of personal weather stations (PWSs) providing high spatial and temporal resolution of rainfall measurements. The highly-localized data collected from these citizen-owned and operated PWSs can effectively bridge the data and information gaps that hamper accurate flood prediction for evidence-based decision-making. However, trust gaps remain in utilizing data from these crowdsourced networks, and methods to measure the trustworthiness of the crowdsourced data in real-time are lacking. In this paper, we present a Reputation System for Crowdsourced Rainfall Networks (RSCRN) to assign trust scores to PWSs based on its agreement, or lack of agreement, with neighboring stations. Using 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 converged to a confident trust score for a PWS within the first 10-20 observations from a station and robustly responded to 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 and maintain. The results indicate that the trust score derived from the RSCRN can reflect the collective measure of trustworthiness of the PWS, bridging the trust gap for modeling and decision-making in the future.