Rainpour an automated interpreter of variable precipitation forecast images - Racho, Samantha May
Flood characteristics can be predicted through the use of precipitation models that require rainfall data. A common form of this data is color-configured forecast maps such as the NOGAPS, and eTRaP images. This paper explores a method to automate the interpretation of these maps to improve the efficiency of flood forecasting in the Philippines. This is achieved by solving two computational sub-problems: (1) recognizing and locating the Philippines in a map, and (2) interpreting the values of the pixels corresponding to the rain gauge stations depending on the configuration on the map. The method presented uses an existing image processing technique called template matching to solve the localization problem, and a look-up vector that is implemented using the HSV (Hue Saturation Value) color space to determine the corresponding configuration values for interpretation problem. It employs an image registration to adapt to various map configurations. By being able to automatically interpret variable precipitation images, flood forecasting can be expedited.