Modeling Red Pine Tree Survival with an Artificial Neural Network
Abstract:A model based on an Artificial Neural Network (ANN) was developed for modeling and predicting red pine survival. The new model uses diameter at breast height and estimated annual diameter growth as its predictors. For training neural networks, a proportional coding scheme based on Gaussian distributions was used to transform the data into patterns of activities. Four model performance criteria--sum of square errors (SSE), ² statistic, final predicted error (FPE), and predicted squared error (PSE)--were used to determine the adequacy of the new model. Based on the four criteria, the ANN-based new red pine survival model not only fits the data better than a statistical model; it is also expected to perform better on future data, provided that the training data are representative. The response surface of the ANN model shows it has the required flexibility to model red pine survival, especially in modeling both small and large but slow growing trees. This study also shows that a proportionally coded training data set may indeed be an effective form of input data representation for developing red pine survival models based on artificial neural networks. For. Sci. 37(5):1429-1440.
Document Type: Journal Article
Affiliations: Associate Professor of Forest Biometrics, Department of Forestry, University of Illinois, 110 Mumford Hall, 1301 West Gregory Drive, Urbana, IL 61801
Publication date: 1991-11-01
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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