% Assuming you have DBH (Diameter at Breast Height), stand volume, and tree height data stored in variables A, B, and C respectively.
data = readmatrix('.xlsx'); % Replace with your Excel filename
% Extract data column(s)
A = data(:, 1); % DBH
B = data(:, 2); % forest age
C= data(:, 3); % forest height
% Merge input and output data
X = [A, B]';
Y = C';
% Construct a neural network architecture
hiddenLayerSize = 10; % Configure hidden layer dimensionality
net = fitnet(hiddenLayerSize); % Implement a Multilayer Perceptron architecture
net.trainParam.lr = 0.01; % Initialize the learning rate as 0.01
% Set the parameters of the neural network and practise
net = train(net, X, Y); % Train the model
% Predict with model rows and columns
predictedBiomass = net(X);
% Miscalculations
rmse = sqrt(mean((Y - predictedBiomass').^2));
mse = mean((Y - predictedBiomass').^2);
% Displays errors and inconsistencies
disp(['RMSE of deep learning models：', num2str(rmse)]);
disp(['MSE of deep learning models：', num2str(mse)]);