이 논문에서는 Washita '92 자료를 이용하여 토양수분의 1차원 및 2차원 통계특성을 추출하였다. 아울러, 토양수분과 토양, 밝기온도(brightness temperature), 식생지수 사이의 상관관계가 어떤지를 선형회귀분석에 근거하여 조사해 보았으며 결과로서 토양수분은 밝기온도와 유의할만한 상관성이 있는 것으로 나타났다. 토양수분의 시간에 대한 감쇠(decay)계수를 각각의 토양군별로 추정하였고, 역으로 이 값을 이용하여 관측전 마지막 강우의

This study is to evaluate the overall NPP(Net Primary Production) distribution in the Korean Peninsula from the satellite data(NOAA/AVHRR). This has been done using the linear relationship between the natural vegetation condition and the NPP. The NPP of natural vegetation increases proportional to the annual net radiation(Rn), where radiative dryness index(RDI) is a proportional constant connecting Rn to NPP. Normalized Difference Vegetation Index(NDVI) is used for monitoring vegetation change, and iNDVI (integrated NDVI) for annual analysis. The iNDVI has a close relation to Rn and NPP, which can be used effectively for estimating NPP distribution of where the meteorological data is unavailable such as North Korea. The NPP distribution of the Korean Peninsula was estimated based on the model.

상관계수가 변수간의 선형 상관관계를 나타내듯이 mutual information은 변수간의비선형 상관관계를 나타내준다. 본 논문에서는 mutual information 추정법으로 다변수 핵 미도함수(multivariate kernel density estimator)를 이용한 방법이 여러 time lags값에 대하여 산정 되었다. 많은 수문자료에서 보여지는 비선형 관계를 Mutual Information으로 확인하여 보았고, 또한 Mutual Infor

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical intelligence technique. The input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rainfall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of rainfall.

The purpose of this study is to develop the data structure for querying database of a rural house design. It is necessary, for developing the data structure, to define items of rural houses and to study about the processing of the data. The classifications of rural houses can be differed in the subjective opinion for computerization. In this study, items of classified rural houses can be expressed numerically, like size and number of room. The user who has insufficient knowledge about house is hard to input items fit personal taste. The querying algorithm was developed using the relationship of items and functions of house. Functions of house mean family life-style and how they use space.