Application of Multilayer Feedforward Neural Networks in predicting tree height and forest stock volume of Chinese fir

Wood increment is critical information in forestry management. Previous studies used mathematics models to describe complex growing pattern of forest stand, in order to determine the dynamic status of growing forest stand in multiple conditions. In our research, we aimed at studying non-linear relationships to establish precise and robust Artificial Neural Networks (ANN) models to predict the precise values of tree height and forest stock volume based on data of Chinese fir. Results show that Multilayer Feedforward Neural Networks with 4 nodes (MLFN-4) can predict the tree height with the lowest RMS error (1.77); Multilayer Feedforward Neural Networks with 7 nodes (MLFN-7) can predict the forest stock volume with the lowest RMS error (4.95). The training and testing process have proved that our models are precise and robust.

[1]  C. C. Grier,et al.  Above- and below-ground net production in 40-year-old Douglas-fir stands on low and high productivity sites , 1981 .

[2]  Amir Hossein Alavi,et al.  Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing , 2011 .

[3]  Graham R. Wood,et al.  Hesitant adaptive search for global optimisation , 1998, Math. Program..

[4]  Qeethara Al-Shayea.  Artificial Neural Networks in Medical Diagnosis , 2024, International Journal of Research Publication and Reviews.

[5]  Yufang Jin,et al.  A conceptual cellular interaction model of left ventricular remodelling post-MI: dynamic network with exit-entry competition strategy , 2010, BMC Systems Biology.

[6]  J. Havel,et al.  Artificial neural networks in medical diagnosis , 2013 .

[7]  Jianqiu Zhang,et al.  Mathematical modeling and stability analysis of macrophage activation in left ventricular remodeling post-myocardial infarction , 2012, BMC Genomics.

[8]  W. H. Smith,et al.  Cone distribution in crowns of Slash Pine (Pinus elliottii Engelm.) in relation to stem, crown and wood increment , 1969 .

[9]  Wang Dongliang.  Distillation range correlation method of gas chromatography for industrial application , 2011 .

[10]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[11]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[12]  Donald H. Burn,et al.  Discussion of “Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms” by Jan Adamowski and Christina Karapataki , 2012 .

[13]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[14]  Voichita Bucur,et al.  An ultrasonic method for measuring the elastic constants of wood increment cores bored from living trees , 1983 .

[15]  Xiaolin Zhang,et al.  Targeting myocardial infarction-specific protein interaction network using computational analyses , 2011, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS).