STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY
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| Format: | Artículo científico |
| Language: | en |
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Universidade Federal de Lavras
2018
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| _version_ | 1876481671234060288 |
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| author | Oytun Emre Sakici |
| author_facet | Oytun Emre Sakici |
| contents | STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY Oytun Emre Sakici Gulay Ozdemir Agrociencias Stem profile Machine learning Transfer function Network architecture Development of artificial neural network (ANN) models to estimate stem tapers of individual trees in mixed Fagus orientalis and Abies nordmanniana subsp. Equi-trojani stands distributed in Karabük region of Turkey, and comparison of the ANN models with stem taper equations were aimed in this study. The measurements were obtained from 516 sample trees (238 for Oriental beech and 278 for Kazdağı fir) in mixed stands of Karabük region. The measurements included diameter at breast height, tree height, diameter at stump height, and diameters at intervals of 1 m along the stem. In total, 45 ANN models and four stem taper equations were developed. Estimation performances of ANN models and stem taper equations were compared using relative rankings according to seven goodness-of-fit criteria. As a result, the ANN models were more successful in estimation of stem taper for both tree species. The most successful ANN model structures were (i) the model using logistic function in hidden layer with 10 nodes and hyperbolic tangent function in output layer for Fagus orientalis, and (ii) the model using logistic function in hidden layer with 10 nodes and linear function in output layer for Abies nordmanniana subsp. Equi-trojani. 2018 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74460240016 https://www.redalyc.org/journal/744/74460240016/ https://www.redalyc.org/journal/744/74460240016/html/ https://www.redalyc.org/journal/744/74460240016/74460240016.epub https://www.redalyc.org/journal/744/74460240016/movil 10.1590/01047760201824042572 en http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.4 Vol.24 |
| format | Artículo científico |
| id | redalyc_74460240016 |
| institution | Redalyc |
| language | en |
| publishDate | 2018 |
| publisher | Universidade Federal de Lavras |
| spellingShingle | STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY Oytun Emre Sakici Agrociencias Stem profile Machine learning Transfer function Network architecture STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY Oytun Emre Sakici Gulay Ozdemir Agrociencias Stem profile Machine learning Transfer function Network architecture Development of artificial neural network (ANN) models to estimate stem tapers of individual trees in mixed Fagus orientalis and Abies nordmanniana subsp. Equi-trojani stands distributed in Karabük region of Turkey, and comparison of the ANN models with stem taper equations were aimed in this study. The measurements were obtained from 516 sample trees (238 for Oriental beech and 278 for Kazdağı fir) in mixed stands of Karabük region. The measurements included diameter at breast height, tree height, diameter at stump height, and diameters at intervals of 1 m along the stem. In total, 45 ANN models and four stem taper equations were developed. Estimation performances of ANN models and stem taper equations were compared using relative rankings according to seven goodness-of-fit criteria. As a result, the ANN models were more successful in estimation of stem taper for both tree species. The most successful ANN model structures were (i) the model using logistic function in hidden layer with 10 nodes and hyperbolic tangent function in output layer for Fagus orientalis, and (ii) the model using logistic function in hidden layer with 10 nodes and linear function in output layer for Abies nordmanniana subsp. Equi-trojani. 2018 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74460240016 https://www.redalyc.org/journal/744/74460240016/ https://www.redalyc.org/journal/744/74460240016/html/ https://www.redalyc.org/journal/744/74460240016/74460240016.epub https://www.redalyc.org/journal/744/74460240016/movil 10.1590/01047760201824042572 en http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.4 Vol.24 |
| title | STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY |
| topic | Agrociencias Stem profile Machine learning Transfer function Network architecture |
| url | https://www.redalyc.org/articulo.oa?id=74460240016 https://www.redalyc.org/journal/744/74460240016/ https://www.redalyc.org/journal/744/74460240016/html/ https://www.redalyc.org/journal/744/74460240016/74460240016.epub https://www.redalyc.org/journal/744/74460240016/movil |