Improving the Efficiency of Forest Cover Forecasting using ANFIS
Sony Ahuja11, Aarti Karandikar2
1Sony Ahuja, Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
2Prof. Aarti Karandikar, Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 282-287 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10770688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Study and analysis of forest cover is a crucial area of research due to the increasing effects of global warming. Scientists have devised various algorithms which not only analyze but help in predicting the forest cover for a particular area based on the parameters extracted from that area. In this paper, we propose a forest cover prediction algorithm which is based on Artificial Neural networks & Fuzzy Inference System (ANFIS), which uses the image analysis data for prediction. The work shows that the prediction accuracy of our proposed ANFIS system is superior to that of the standard neural networks. The datasets used are taken from standard GIS websites in order to evaluate the prediction and validate the same.
Keywords: ANFIS, Forest cover, fuzzy, neural, prediction.
Scope of the Article: Fuzzy Logics