한국물환경학회지 (Journal of Korean Society on Water Environment)

  • 제29권1호
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  • Pages.19-28
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  • 2013
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  • 2289-0971(pISSN)
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  • 2289-098X(eISSN)
한국물환경학회 (Korean Society on Water Environment)
권호 표지

인공신경망을 이용한 팔당호의 조류발생 모델 연구

Study on the Modelling of Algal Dynamics in Lake Paldang Using Artificial Neural Networks

  • 박혜경 (국립환경과학원 물환경연구부) ;
  • 김은경 (국립환경과학원 물환경연구부)
  • Park, Hae-Kyung (Water Environment Research Department, National Institute of Environmental Research) ;
  • Kim, Eun-Kyoung (Water Environment Research Department, National Institute of Environmental Research)
  • 발행 : 2013.01.30
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

키워드

참고문헌

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