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The records of 24,797 traffic accidents (9039 involving fatalities or severe injury) during rainy conditions from 2007 to 2017 in Seoul, South Korea, were used to analyze the spatial distribution of the traffic accidents and rainfall events based on radar and gauge rainfall data. According to the spatial correspondence analysis between rainfall distribution and accident locations for localized and stratiform rain events, radar data in a two-dimensional grid (250 by 250 m) of 10 min temporal resolution benefited the localized rainfall distribution concerning the accident location. The relative accident rate (RAR) from radar data, which was used as a quantitative reference value for the effect of rainfall on traffic accidents, was about 18% higher than that from gauge rainfall. The radar data more clearly classified the number of traffic accidents during rainy conditions because its spatial distribution was more precise for all accidents. In addition, the RAR estimation of accidents involving fatalities and severe injury during rainfall could provide information on the district in which traffic accidents increase due to rainfall. The study results support the adoption of radar-derived rainfall data to analyze the influence of rainfall on accidents and the development of more accurate risk-assessment tools for drivers and planners. [ABSTRACT FROM AUTHOR]

Water level data sets acquired by ultrasonic sensors in stream-scale channels exhibit relatively large numbers of outliers that are off the measurement range between the ultrasonic sensor and water surface, as well as data dispersion of approximately 2 cm due to random errors such as water waves. Therefore, this study develops a data processing algorithm for outlier removal and smoothing for water level data measured by ultrasonic sensors to consider these characteristics. The outlier removal process includes an initial cutoff process to remove outliers out of the measurement range and an outlier detection process using modified Z-scores based on the median absolute deviation (MAD) of a robust estimator. In addition, an exponentially weighted moving average (EWMA) method is applied to smooth the processed data. Sensitivity analyses are performed for factors that are subjectively set by the user, including the window size for the MAD outlier detection stage, the rejection criterion for the modified Z-score outlier removal stage, and the smoothing constant for the EWMA smoothing stage, based on four different water level data sets acquired by ultrasonic sensors in stream-scale experiments. [ABSTRACT FROM AUTHOR]