Configuration of the Deep Neural Network Hyperparameters for the Hypsometric Modeling of the?Guazuma crinita?Mart. in the Peruvian Amazon

编号 030034403

推送时间 20220523

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Configuration of the Deep Neural Network Hyperparameters for the Hypsometric Modeling of the?Guazuma crinita?Mart. in the Peruvian Amazon

来源期刊 Forests

期 第344期

发表时间 20220429

关键词 deep learning;  artificial neural network;  total height;  forest management; 

摘要 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.

服务人员 付贺龙

服务院士 唐守正

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