Forest Type Classification
CHAPTER 5
FOREST TYPE CLASSIFICATION USING ARTIFICIAL NEURAL NETWOKS
Forest type classification is an important problem in order to take future decisions, for the administrators. The conservation of forests is an important activity to be undertaken by forest managers. If they can predict the type of forest based on some initial parameters, probably they can plan their policies efficiently for conserving the forests. In such a situation, the policy makers and foresters can evolve suitable strategies for appropriate management interventions for sustainable forest management, through participatory approach. It has been noted that there exists no significant study on forest type classification in India, using neural network models. There are,
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We have experimented with two training functions in order to predict the forest type. We have also conducted the experiments for number of neurons in the hidden layer. After experimenting with a number of neurons in the hidden layer, those results that could converge for the specified number of neurons in the hidden layer have been presented. It is worth mentioning here that we experimented with a number of other combinations, for the hidden neurons in the hidden …show more content…
This model was trained in 5762 iterations.
Figure 5.8: Predicted forest type as a function of forest type using ANN model with 100 neurons in hidden layer and trainscg function
This study has further been extended to include 110 and 120 neurons in the hidden layer. The training function ‘trainscg’ has been repeated for the training of these two ANN models. The results of these two experiments are prseneted in Figure 5.9, and Figure 5.10 on page 53 respectively. It was noted that the ANN model with 110 neurons in hidden layer could be trained in 6366 iterations and the ANN model with 120 neurons in the hidden layer could get trained in 3940 iterations, in order to achieve an MSE of 0.5. Figure 5.9: Predicted forest type as a function of fores type using ANN model with 110 neurons in hidden layer and trainscg function t
Figure 5.10: Predicted forest type as a function of forest type using ANN model with 120 neurons in hidden layer and trainscg
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