Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon
The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different...
| Main Authors: | , , , , |
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| Format: | Artículo |
| Language: | Español |
| Published: |
MDPI
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.12955/2086 https://doi.org/10.3390/f13050697 |
| _version_ | 1855490291560087552 |
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| author | Goycochea Casas, Gianmarco Elera Gonzáles, Duberlí Geomar Baselly Villanueva, Juan Rodrigo Pereira Fardin, Leonardo Garcia Leite, Hélio |
| author_browse | Baselly Villanueva, Juan Rodrigo Elera Gonzáles, Duberlí Geomar Garcia Leite, Hélio Goycochea Casas, Gianmarco Pereira Fardin, Leonardo |
| author_facet | Goycochea Casas, Gianmarco Elera Gonzáles, Duberlí Geomar Baselly Villanueva, Juan Rodrigo Pereira Fardin, Leonardo Garcia Leite, Hélio |
| author_sort | Goycochea Casas, Gianmarco |
| collection | Repositorio INIA |
| description | The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others. |
| format | Artículo |
| id | INIA2086 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Español |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA20862023-06-21T15:39:46Z Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon Goycochea Casas, Gianmarco Elera Gonzáles, Duberlí Geomar Baselly Villanueva, Juan Rodrigo Pereira Fardin, Leonardo Garcia Leite, Hélio Deep learning Artificial neural network Total height Forest management https://purl.org/pe-repo/ocde/ford#4.01.02 Forest management The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others. 2023-02-23T15:25:15Z 2023-02-23T15:25:15Z 2022-04-29 info:eu-repo/semantics/article Casas, G. G., Gonzáles, D. G. E., Villanueva, J. R. B., Fardin, L. P., & Leite, H. G. (2022). Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon. Forests, 13(5), 697. doi: https://doi.org/10.3390/f13050697 1999-4907 https://hdl.handle.net/20.500.12955/2086 https://doi.org/10.3390/f13050697 spa urn:issn:1999-4907 Forests info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Deep learning Artificial neural network Total height Forest management https://purl.org/pe-repo/ocde/ford#4.01.02 Forest management Goycochea Casas, Gianmarco Elera Gonzáles, Duberlí Geomar Baselly Villanueva, Juan Rodrigo Pereira Fardin, Leonardo Garcia Leite, Hélio Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon |
| title | Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon |
| title_full | Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon |
| title_fullStr | Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon |
| title_full_unstemmed | Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon |
| title_short | Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart. in the Peruvian Amazon |
| title_sort | configuration of the deep neural network hyperparameters for the hypsometric modeling of the guazuma crinita mart in the peruvian amazon |
| topic | Deep learning Artificial neural network Total height Forest management https://purl.org/pe-repo/ocde/ford#4.01.02 Forest management |
| url | https://hdl.handle.net/20.500.12955/2086 https://doi.org/10.3390/f13050697 |
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