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A Survey of Applications of Artificial Intelligence Algorithms in Eco-environmental Modelling
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  • Journal title : Environmental Engineering Research
  • Volume 14, Issue 2,  2009, pp.102-110
  • Publisher : Korean Society of Environmental Engineering
  • DOI : 10.4491/eer.2009.14.2.102
 Title & Authors
A Survey of Applications of Artificial Intelligence Algorithms in Eco-environmental Modelling
Kim, Kang-Suk; Park, Joon-Hong;
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Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.
Eco-environmental modeling;Data mining;Artificial intelligence;Decision Tree (DT);Artificial Neural Network (ANN);Training data;Prediction accuracy;
 Cited by
갯벌 미생물 다양성 모니터링 시료 채취 개수 및 간격 선정을 위한 지구통계학적 기법과 데이터 마이닝 적용 연구,양지훈;이재진;유근제;박준홍;

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