Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors
Models of forest growth and yield provide important information on stand and tree developments and the interactions of these developments with silvicultural treatments. These models have been developed based on assumptions such as independence of observations, uncorrelated error terms, and error ter...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Italian Society of Silviculture and Forest Ecology (SISEF)
2023-02-01
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| Series: | iForest - Biogeosciences and Forestry |
| Subjects: | |
| Online Access: | https://iforest.sisef.org/contents/?id=ifor4116-015 |
| _version_ | 1797945630640832512 |
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| author | Bolat F Ercanli I Günlü A |
| author_facet | Bolat F Ercanli I Günlü A |
| author_sort | Bolat F |
| collection | DOAJ |
| description | Models of forest growth and yield provide important information on stand and tree developments and the interactions of these developments with silvicultural treatments. These models have been developed based on assumptions such as independence of observations, uncorrelated error terms, and error terms with constant variance; if these factors are absent, there may be problems with multicollinearity, autocorrelation, or heteroscedasticity, respectively. These problems, which have several adverse effects on parameter estimates, are statistical phenomena and must be avoided. In recent years, the artificial neural network (ANN) model, thanks to its superior features such as the ability to make successful predictions and the absence of the requirement for statistical assumptions, has been commonly used in forestry modeling. However, while goodness-of-fit measures were taken into consideration in the assessment of ANN models, the control of the biological characteristics of model predictions was ignored. In this study, variable-density yield models were developed using nonlinear regression and ANN techniques. These modeling techniques were compared based on some goodness-of-fit measures and the principles of forest yield. The results showed that ANN models were more successful in meeting expected biological patterns than regression models. |
| first_indexed | 2024-04-10T20:58:11Z |
| format | Article |
| id | doaj.art-cbfa74cb0ab04b198dc0aa31505de7a7 |
| institution | Directory Open Access Journal |
| issn | 1971-7458 |
| language | English |
| last_indexed | 2024-04-10T20:58:11Z |
| publishDate | 2023-02-01 |
| publisher | Italian Society of Silviculture and Forest Ecology (SISEF) |
| record_format | Article |
| series | iForest - Biogeosciences and Forestry |
| spelling | doaj.art-cbfa74cb0ab04b198dc0aa31505de7a72023-01-22T19:07:40ZengItalian Society of Silviculture and Forest Ecology (SISEF)iForest - Biogeosciences and Forestry1971-74582023-02-01161303710.3832/ifor4116-0154116Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviorsBolat F0Ercanli I1Günlü A2Çankiri Karatekin University, Faculty of Forestry, 18200, Çankiri - TurkeyÇankiri Karatekin University, Faculty of Forestry, 18200, Çankiri - TurkeyÇankiri Karatekin University, Faculty of Forestry, 18200, Çankiri - TurkeyModels of forest growth and yield provide important information on stand and tree developments and the interactions of these developments with silvicultural treatments. These models have been developed based on assumptions such as independence of observations, uncorrelated error terms, and error terms with constant variance; if these factors are absent, there may be problems with multicollinearity, autocorrelation, or heteroscedasticity, respectively. These problems, which have several adverse effects on parameter estimates, are statistical phenomena and must be avoided. In recent years, the artificial neural network (ANN) model, thanks to its superior features such as the ability to make successful predictions and the absence of the requirement for statistical assumptions, has been commonly used in forestry modeling. However, while goodness-of-fit measures were taken into consideration in the assessment of ANN models, the control of the biological characteristics of model predictions was ignored. In this study, variable-density yield models were developed using nonlinear regression and ANN techniques. These modeling techniques were compared based on some goodness-of-fit measures and the principles of forest yield. The results showed that ANN models were more successful in meeting expected biological patterns than regression models.https://iforest.sisef.org/contents/?id=ifor4116-015BayesianMachine LearningGompertzOverfitting |
| spellingShingle | Bolat F Ercanli I Günlü A Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors iForest - Biogeosciences and Forestry Bayesian Machine Learning Gompertz Overfitting |
| title | Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors |
| title_full | Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors |
| title_fullStr | Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors |
| title_full_unstemmed | Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors |
| title_short | Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors |
| title_sort | yield of forests in ankara regional directory of forestry in turkey comparison of regression and artificial neural network models based on statistical and biological behaviors |
| topic | Bayesian Machine Learning Gompertz Overfitting |
| url | https://iforest.sisef.org/contents/?id=ifor4116-015 |
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