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한국하천호수학회 / v.43, no.1, 2010년, pp.161-167 
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비선형 시계열 하천생태모형 개발과정 중 시간지연단계와 입력변수, 모형 예측성 간 관계평가
( Relationship among Degree of Time-delay, Input Variables, and Model Predictability in the Development Process of Non-linear Ecological Model in a River Ecosystem ) |
| 정광석;김동균;윤주덕;라긍환;김현우;주기재; 부산대학교 자연과학대학 생명과학과;서울대학교 공과대학 컴퓨터공학부;부산대학교 자연과학대학 생명과학과;순천대학교 사범대학 환경교육과;순천대학교 사범대학 환경교육과;부산대학교 자연과학대학 생명과학과;
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| 초 록 |
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| In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used. |
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| 키워드 |
| input variable selectivity;ecological modeling;Stephanodiscus hantzschii;machine learning;ecological informatics;river ecosystems; |
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한국하천호수학회지 / v.43, no.1, 2010년, pp.161-167
한국하천호수학회
ISSN : 1976-8087
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO201023064630436)
언어 : 한국어 |
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| 논문 제공 : KISTI 한국과학기술정보연구원 |
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