• 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 Association For Science Education
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• v.33 no.1
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• pp.144-160
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• 2013

This study is a proposal, which is the trial to explicate, in neurology, on how critical thinking as a creative method of sciences functions. The creative methods of sciences, even at present, are mostly the hypothetical insistences concerning with the logical processes of researches suggested from the philosophers of science; Popper, Kuhn, Hempel, or Lakatos. These insistences do excavate what process or approach can be scoped out of scientists' creativity. I call the tendency or approach of the researches, "Process Approach of Creativity (PAC)". From my view point, any PAC trial does not concern with how creative theories can actually be invented. On the other hand, this study is focused on the philosophical thinking abilities of scientists who invented new great theories. They mostly had some experiences to study philosophy while studying their science fields, thus had critical thinking abilities on their studies. From my point of view, critical thinking in philosophy raised questions as to their fundamental and basic (old) concepts and principles, and thus gave them new creative theories. I will try to explain this from the point of neurophilosophy. From the perspectives coming from "the state space theory of representation" of Paul & Patricia Churchland, the pioneers of neurophilosphy, the "creative theories" are the networks of topographic maps giving new comprehensive explanations and predictions. From these perspectives, I presuppose that the attitude of critical questioning revises the old networks of maps with back-propagation or feedback, and thus, is the generative power of searching new networks of maps. From the presupposition, I can say, it is important that scientists reflect on the basic premises in their academic branches for issuing out extraordinary creativity. The critical attitude of philosophy can make scientists construct the maps of new conceptual scheme by shaking the maps of the old basic premises. From this context, I am able to propose "Critical Thinking Approach of Creativity (CTAC)".

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.