• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of the Korean earth science society
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• v.29 no.3
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• pp.263-279
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• 2008

This study is concerned with the optimization of models using MM5 and WRF mesoscale numerical models to simulate strong sea surface winds, such as that of typhoon Shanshan on 17 September 2006, and the Siberian high event on 16 December 2006, which were selected for displaying the two highest mean wind speeds. The model optimizations for the lowest level altitude, physical parameters and horizontal resolution were all examined. The sea surface wind values obtained using a logarithmic function which takes into account low-level stability and surface roughness were more accurate than those obtained by adjusting the lowest-level of the model to 10 m linearly. To find the optimal parameters for simulating strong sea surface winds various physical parameters were combined and applied to the model. Model grid resolutions of 3-km produced better results than those of 9-km in terms of displaying accurately regions of strong wind, low pressure intensities and low pressure mesoscale structures.