Forest Cover Classification Using Stacking of Ensemble Learning and Neural Networks

Deforestation is one of the major issues, that is, being affecting the environment for the long time and there are few effective measures have been taken to withstand it and to maintain the pristine of the nature. One of them is preserving the wilder forests. The main motive of the proposed work is to classify the forest dataset so that it helps the authorities in maintaining the forests and protecting them by controlled deforestation and re-growing. The proposed classification technique introduces the stacking approach of Ensemble learning which uses random forests, extra trees with boosting and multilayered perceptron techniques for forest cover classification. The proposed model is evaluated using dataset from the UCI library. The proposed stacking approach shows the improvement in the quality of forest covers classification results and is shown using ROC curve analysis.