• Yada Sindhura
• Golla Usha Rani
• Joseph Fathima Rosme

Natural resource planning is an important aspect for any society. Knowing forest cover type is one of the Forest land is highly required for developing ecosystem management. Any changes that occur in ecosystem should be carefully noticed to avoid further loss. This model is helpful in noticing the changes occurred due to heavy floods or any other calamities which affected the forest land. A machine learning Algorithm is used to predict the forest cover type using the cartographic variables. The approach is to predict the forest cover type using the cartographic variables like aspect, slope, soil type, wilderness area etc. Various Data mining techniques such as decision tress, random forest, regression trees, and gradient boosting machines are used for prediction of the forest cover type.

regression,random,wilderness,gradient,prediction,calamites,ecosystem

"MACHINE LEARNING TECHNIQUES TO PREDICT FOREST COVER BY USING CARTOGRAPHIC VARIABLES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.f422-f430, February-2024, Available :http://www.jetir.org/papers/JETIR2402552.pdf

"MACHINE LEARNING TECHNIQUES TO PREDICT FOREST COVER BY USING CARTOGRAPHIC VARIABLES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppf422-f430, February-2024, Available at : http://www.jetir.org/papers/JETIR2402552.pdf