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논문 기본 정보

자료유형
학술저널
저자정보
Sang-Hoon Oh (Mokwon University)
저널정보
한국콘텐츠학회(IJOC) International JOURNAL OF CONTENTS International JOURNAL OF CONTENTS Vol.13 No.4
발행연도
2017.12
수록면
23 - 28 (6page)

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Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP’s are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP’s are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site “Hahoe Village,” we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85㎝ vs. 55.51㎝.

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ABSTRACT
1. INTRODUCTION
2. ERROR BACK-PROPAGATION ALGORITHM WITH A MODIFIED ERROR FUNCTION
3. HYDROLOGICAL MODELING NEAR “HAHOE VILLAGE”
4. CONCLUSIONS
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