Individual Growth Model for Eucalyptus Stands in Brazil Using Artificial Neural Network

Table 3

Output and input variables for neural network training.

Output variable	Input variables	Number of trained networks
𝑃𝑚	𝐼1, 𝑆, CLA, IID1	100
	𝐼1, 𝑆, CLA, IID2	100
	𝐼1, 𝑆, CLA, IID3	100
	𝐼1, 𝑆, CLA, IID4	100
	𝐼1, 𝑆, CLA, IID5	100
Total		500
Ht2	𝐼1, 𝐼2, 𝑆, Ht1, IID1	100
	𝐼1, 𝐼2, 𝑆, Ht1, IID2	100
	𝐼1, 𝐼2, 𝑆, Ht1, IID3	100
	𝐼1, 𝐼2, 𝑆, Ht1, IID4	100
	𝐼1, 𝐼2, 𝑆, Ht1, IID5	100
Total		500
d.b.h.2	𝐼1, 𝐼2, 𝑆, d.b.h.1, IID1	100
	𝐼1, 𝐼2, 𝑆, d.b.h.1, IID2	100
	𝐼1, 𝐼2, 𝑆, d.b.h.1, IID3	100
	𝐼1, 𝐼2, 𝑆, d.b.h.1, IID4	100
	𝐼1, 𝐼2, 𝑆, d.b.h.1, IID5	100
Total		500

𝑃_𝑚: annual mortality probability; Ht₂: height at a future age (m); d.b.h.₂: diameter at a future age (cm); 𝐼₁ and 𝐼₂: current and future ages (months); 𝑆: site index (m); CLA: diameter class in which the tree was (cm); d.b.h.₁ and Ht₁: current diameters (cm) and heights (m); and IID₁ to IID₅: competition indices.