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퍼지론에 의한 강수예측 : I. 뉴로-퍼지 시스템과 마코프 연쇄의 적용

Precipitation forecasting by fuzzy Theory : I - Applications of Neuro-fuzzy System and Markov Chain

  • 발행 : 2002.10.01

초록

대기에서의 물순환은 기후시스템이라는 커다란 공간 안에서 다양한 인자들의 상호작용을 통하여 이루어진다. 즉, 어떠한 기후 현상도 그 자체적으로 발생할 수는 없다. 따라서, 많은 연구자들은 영향인자들의 분석을 통하여 기후 변화를 이해하고자 노력하여 왔다. 본 연구에서는 다양한 인자에 의하여 영향을 받아 발생하는 강수량의 예측을 위하여 실제 세계의 근사적이고 부정확한 성질을 표현하는데 효과적인 퍼지 개념을 이용하였다. 예측을 위하여 적용한 모형은 크게 뉴로-퍼지 시스템과 마코프 연쇄이며, 일리노이주의 강수량 예측을 위하여 적용하였다. 예측은 강수량에 영향을 끼치는 다양한 대기순환 인자(예: 토양수분과 기온)를 고려하여 수행하였다. 예측 결과, 강수량 예측에 대기순환 인자들을 고려함으로써 모형의 예측능력을 향상시킬 수 있었고, 상대적으로 뉴로-퍼지 시스템의 예측이 보다 정확한 결과를 주었다.

Water in the atmosphere is circulated by reciprocal action of various factors in the climate system. Otherwise, any climate phenomenon could not occur of itself. Thus, we have tried to understand the climate change by analysis of the factors. In this study, the fuzzy theory which is useful to express inaccurate and approximate nature in the real world is used for forecasting precipitation influenced by the factors. Forecasting models used in this study are neuro-fuzzy system and a Markov chain and those are applied to precipitation forecasting of illinois. Various atmosphere circulation factors(like soil moisture and temperature) influencing the climate change are considered to forecast precipitation. As a forecasting result, it can be found that the considerations of the factors are helpful to increase the forecastibility of the models and the neuro-fuzzy system gives us relatively more accurate forecasts.

키워드

참고문헌

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