IoT-based early forest fire detection using MLP and AROC method

被引：4

作者：

Vinodhini, V. ^{[1
]}

Kumar, M. R. Sundara ^{[1
]}

Sankar, S. ^{[1
]}

Pandey, Digvijay ^{[2
]}

Pandey, Binay Kumar ^{[3
]}

Nassa, Vinay Kumar ^{[4
]}

机构：

[1] Sona Coll Technol, Dept CSE, Salem 636005, India

[2] Dr APJ Abdul Kalam Tech Univ, Dept Tech Educ, IET, Lucknow, Uttar Pradesh, India

[3] GovindBallabh Pant Univ Agr & Technol, Dept IT, Uk, India

[4] South Point Grp Inst, Dept Comp Sci Engn, Sonepat 131001, India

来源：

INTERNATIONAL JOURNAL OF GLOBAL WARMING | 2022年 / 27卷 / 01期

关键词：

forest fire; internet of things; IoT; artificial neural networks; ANNs; flame sensor; smoke sensor; multi-layer perceptron; MLP;

D O I：

10.1504/IJGW.2022.122794

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The forest is a natural ecosystem that must be protected against natural calamities. Forest fire is one such calamity, and the goal of this work is to alert the event of disaster so that natural resources can be saved. The existing methods have few limitations like false alert, no timely notification, lack of network coverage, etc. The proposed work uses multi-layer perceptron (MLP) and advanced relative operating characteristic (AROC) approaches to address these constraints. The proposed model has accuracy of 90%, which is higher than the fuzzy logic and average consensus algorithm.

引用

页码：55 / 70

页数：16

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