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Modeling red pine tree mortality: An...

Guan, Biing Tzuang.

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Modeling red pine tree mortality: An artificial neural network approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling red pine tree mortality: An artificial neural network approach./
Author:	Guan, Biing Tzuang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 1991,
Description:	172 p.
Notes:	Source: Dissertation Abstracts International, Volume: 52-11, Section: B, page: 5605.
Contained By:	Dissertation Abstracts International52-11B.
Subject:	Forestry. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9210819

Modeling red pine tree mortality: An artificial neural network approach.
Guan, Biing Tzuang.

Modeling red pine tree mortality: An artificial neural network approach. - Ann Arbor : ProQuest Dissertations & Theses, 1991 - 172 p.

Source: Dissertation Abstracts International, Volume: 52-11, Section: B, page: 5605.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1991.

The main objective of this study is to seek new modeling techniques to improve the ability of projecting tree mortality in forest growth and yield simulation. Multi-layer feed-forward artificial neural networks (ANN) are adopted to achieve the goal. The premise of ANN modeling approach is the ability of such networks to approximate any measurable or continuous function to any desired degree of accuracy, given enough complexity and training.Subjects--Topical Terms:

895157
Forestry.

Modeling red pine tree mortality: An artificial neural network approach.
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035 $a AAI9210819
040 $a MiAaPQ $c MiAaPQ
100 1 $a Guan, Biing Tzuang. $3 3283821
245 1 0 $a Modeling red pine tree mortality: An artificial neural network approach.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 1991
300 $a 172 p.
500 $a Source: Dissertation Abstracts International, Volume: 52-11, Section: B, page: 5605.
500 $a Adviser: George Z. Gertner.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1991.
520 $a The main objective of this study is to seek new modeling techniques to improve the ability of projecting tree mortality in forest growth and yield simulation. Multi-layer feed-forward artificial neural networks (ANN) are adopted to achieve the goal. The premise of ANN modeling approach is the ability of such networks to approximate any measurable or continuous function to any desired degree of accuracy, given enough complexity and training.
520 $a In this study, two types of tree mortality models are developed based on red pine (Pinus resinosa Ait.) data collected from the Great Lakes region. Diameter at breast height (DBH) and annual diameter growth (ADG) are the explanatory variables in both types of models, and annual survival rate is the response variable. For the first type of models, training set consists of data obtained based on a cross-classified scheme. For the second type of models, individual tree records are used to construct training set. Training method for the first type of models is the back-propagation method, and networks are trained on serial computers. A method based on the fast simulated annealing is used to train models of the second, and the trainings are performed on a massively parallel computer. In addition to several goodness-of-fit and performance statistics, a model-based comparison approach is also developed to assess the performance of ANN mortality models against a benchmark statistical model.
520 $a Results from this dissertation suggest that ANN mortality models not only fit the training data better than the benchmark model, but also expect to perform better in the future, provided that the training set are representative. Model-based comparisons show that ANN mortality model in general have lower prediction biases, but with larger prediction variances, than the benchmark model. Mean squared error criterion suggests that ANN mortality models are expected to perform better in the future, provided the training data are representative.
520 $a A brief review of modeling tree mortality in forestry growth and yield projection, as well as an overview of neural computing approach, is also presented in this study. Other issues related to the use of artificial neural networks in forestry related modeling are also discussed.
590 $a School code: 0090.
650 4 $a Forestry. $3 895157
650 4 $a Biostatistics. $3 1002712
650 4 $a Artificial intelligence. $3 516317
690 $a 0478
690 $a 0308
690 $a 0800
710 2 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 52-11B.
790 $a 0090
791 $a Ph.D.
792 $a 1991
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9210819