Deforestation probability assessment using integrated machine learning algorithms of Eastern Himalayan foothills (India)

Abstract

The significant biodiversity rich Jaldapara National Park is situated at Terai-Dooars region of Eastern Himalayan foothill. This study attempts to identify the deforestation probable zones at Jaldapara national park and its surroundings applying five different machine learning algorithms (SVM, NB, RF, DT and ANN). Results show that the northern and middle sections are being faced by high rate of deforestation due to large scale human encroachment, poaching and timber trafficking. Result also illustrates that support vector machine (SVM) brings more accuracy compared with other models. These deforestation probable models are validated through receiver operation characteristics, efficiency, sensitivity and specificity measurement. Area under curve (AUC) value of these models is 0.907, 0.885, 0.825, 0.846 and 0.876 respectively. The novelty of this research is that previously, such machine learning methods (with high precision) have not applied to examine the deforestation probability in this region of Himalayan foothill.