Automatic Detection of RWIS Sensor Malfunctions (Phase I)

The overall goal of this project was to develop computerized procedures that detect Road Weather Information System (RWIS) sensor malfunctions. In this phase of the research we applied two classes of machine learning techniques to data generated by RWIS sensors in order to predict sensor malfunctions and thereby improve accuracy in forecasting temperature, precipitation, and other weather-related data. We built models using machine learning methods that employ data from nearby sensors in order to predict likely values of those sensors that are being monitored. A sensor that deviates noticeably from values inferred from nearby sensors indicates that the sensor has begun to fail. We used both classification and regression algorithms in Phase I. In particular, we used three classification algorithms (namely, J48 decision trees, native Bayes, and Bayesian networks) and six regression algorithms (that is, linear regression, least median squares, M5P, multilayer perceptron, radial basis function network, and the conjunctive rule algorithm). We performed a series of experiments to determine which of these models can be used to detect malfunctions in RWIS sensors. We compared the values predicted by the various machine learning methods to the actual values observed at an RWIS sensor to detect sensor malfunctions. This report provides an overview of the nine models used and a classification of the applicability of each model to the detection of RWIS sensor malfunctions.