Fog Occurrence Forecast by Using LVQ with a Genetical Preprocessing

Abstract

A transportation development in recent years is quite remarkable. However, poor visibility often cause an accident. Therefore, it is very important to forecast a fog occurrence. In this paper, we propose a scheme to forecast a fog occurrence by using the Learning Vector Quantization (LVQ) and a Genetic Algorithm (GA). This scheme forecasts the fog occurrence by the weather data which are provided from the Japan Meteorological Agency. First, the provided data formation are shown. Next, the prediction scheme is described in detail. In this method, input attributes for a LVQ network are selected by real-coded GA to improve forecast accuracy. Furthermore, a partial selection processing in the real-coded GA impreoves its convergence properties. Finally, in order to show the effectiveness of the proposed prediction scheme, computer simulations are performed.