Cyclone track forecasting based on satellite images using artificial neural networks
نویسندگان
چکیده
Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone attacks. To mitigate the damages caused by cyclones, improved cyclone forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAAAVHRR satellite images. A multi-layered neural network, resembling the human visual system, was trained to forecast the movement direction of cyclones based on satellite images. The trained network produced correct directional forecast for 98 % of novel test images, thus showing a good generalization capability from training images to novel images. The results indicate that multilayered neural networks could be further developed into an effective tool for cyclone track forecasting using various types of remote sensing data. Future work includes extension of the present network to handle a wide range of cyclones and to take into account supplementary information, such as wind speeds around the cyclone, water temperature, air moisture, and air pressure.
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