APPLICATION OF AN ARTIFICIAL NEURAL NETWORK TO IMPROVE SHORT-TERM ROAD ICE FORECASTS

Abstract This paper describes how a three-layer artificial neural network (NN) can be used to improve the accuracy of short-term (3–12 hours) automatic numerical prediction of road surface temperature, in order to cut winter road maintenance costs, reduce environmental damage from oversalting and provide safer roads for road users. In this paper, the training of the network is based on historical and preliminary meteorological parameters measured at an automatic roadside weather station, and the target of the training is hourly error of original numerical forecasts. The generalization of the trained network is then used to adjust the original model forecast. The effectiveness of the network in improving the accuracy of numerical model forecasts was tested at 39 sites in eight countries. Results of the tests show that the NN technique is able to reduce absolute error and root-mean-square error of temperature forecasts by 9.9–29%, and increase the accuracy of frost/ice prediction.

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