A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach. (September 2016)

• Record Type: Journal Article
• Authors: Rao, Gudikandhula Narasimha; Rao, Peddada Jagadeeswara; Duvvuru, Rajesh
• Is Part Of: IOP conference series. Volume 149(2016)
• Publication Date: 2016-09
• Subjects: Drone Remote Sensing -- Semantic Neural network -- Machine Learning -- Fire Simulator; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/149/1/012011
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.