A Survey of Applications of Artificial Intelligence Algorithms in
Eco-environmental Modelling |
Kangsuk Kim, and Joonhong Park† |
School of Civil and Environmental Engineering, Yonsei University, Seoul 120-74, Republic of Korea |
Corresponding Author:
Joonhong Park ,Tel: +82-2-2123-5798, Fax: +82-2-312-5798, Email: parkj@yonsei.ac.kr |
Received: January 8, 2009; Accepted: April 7, 2009. |
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ABSTRACT |
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. |
Keywords:
Eco-environmental modeling | Data mining | Artificial intelligence | Decision Tree (DT) | Artificial Neural Network (ANN) | Training data |
Prediction accuracy |
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