Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human ... more Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth' rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.
ABSTRACT Biologically inspired hierarchical networks for image processing are based on parallel f... more ABSTRACT Biologically inspired hierarchical networks for image processing are based on parallel feature extraction across the image using feature detectors that have a limited Receptive Field (RF). It is, however, unclear how large these receptive fields should be. To ...
Visual pattern recognition is a complex problem, and it has proven difficult to achieve satisfact... more Visual pattern recognition is a complex problem, and it has proven difficult to achieve satisfactorily instandard three-layer feed-forward artificial neural networks. For this reason, an increasing number ofresearchers are using networks whose architecture resembles ...
Isprs Journal of Photogrammetry and Remote Sensing, 2009
Many places around the world are exposed to tropical cyclones and associated storm surges. In spi... more 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 NOAA-AVHRR 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.
Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human ... more Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth' rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.
ABSTRACT Biologically inspired hierarchical networks for image processing are based on parallel f... more ABSTRACT Biologically inspired hierarchical networks for image processing are based on parallel feature extraction across the image using feature detectors that have a limited Receptive Field (RF). It is, however, unclear how large these receptive fields should be. To ...
Visual pattern recognition is a complex problem, and it has proven difficult to achieve satisfact... more Visual pattern recognition is a complex problem, and it has proven difficult to achieve satisfactorily instandard three-layer feed-forward artificial neural networks. For this reason, an increasing number ofresearchers are using networks whose architecture resembles ...
Isprs Journal of Photogrammetry and Remote Sensing, 2009
Many places around the world are exposed to tropical cyclones and associated storm surges. In spi... more 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 NOAA-AVHRR 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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Papers by Chandan Roy