ESTIMATING RAINFALL IN THE PHILIPPINES USING AN AUTOMATED INTERPRETATION OF FORECAST IMAGES

Authors

  • Tristan Basa Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines
  • Paul Rossener Regonia Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines
  • Samantha May Racho Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines
  • Prospero Naval Jr. Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines

DOI:

https://doi.org/10.11113/aej.v2.15378

Abstract

Flood forecasting is a process that relies on hydrologic models to predict water levels and flow rates in different basins. These hydrologic models depend on the predicted amount of water in rain clouds. A common form of this data for these models comes from color-configured forecast map images. These images are manually interpreted. However, manual interpretation is slow, tedious, and prone to error especially if there are numerous images. We propose a method to automate the interpretation of these images for a faster and more efficient means to predict the amount of water in the clouds. We identify two computational sub-problems: (1) localization and recognition of the region of interest (ROI), and (2) interpretation of the values in the ROI. We use the Speed-up Robust Features (SURF) technique to localize the ROI‟s, and a look-up table which makes use of Hue Saturation Value (HSV) color space. Experimental results show higher accuracy compared to the manual interpretation, and a significantly faster processing time.

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Published

2012-09-13

Issue

Section

Environmental Engineering

How to Cite

ESTIMATING RAINFALL IN THE PHILIPPINES USING AN AUTOMATED INTERPRETATION OF FORECAST IMAGES. (2012). ASEAN Engineering Journal, 2(2), 97-104. https://doi.org/10.11113/aej.v2.15378