NDLI: A methodology for fire data analysis based on pattern recognition towards the disaster management

Content Provider	IEEE Xplore Digital Library
Author	Rasouli, Simin Granmo, Ole-Christoffer Radianti, Jaziar
Copyright Year	2015
Description	Author affiliation: Department of Information and Communication Technology, University of Agder, Grimstad, Norway (Rasouli, Simin; Granmo, Ole-Christoffer; Radianti, Jaziar)
Abstract	The aim of this paper is to investigate a proposed strategy for fire disaster analysis that is implemented based on pattern recognition technique in order to achieve a methodology for disaster management. Since the fire hazard has severe effects onto human and properties, it is essential to predict and possibly prevent it. Almost every fire produces some issues, such as heat, smoke, gas, and flame, which are sensible and measurable via devices or detection systems. The fire behavior is relevant to these issues. In this research, temperature, heat radiation, and visibility (smoke) data of fire that have been obtained from Fire Dynamics Simulator (FDS) are used for analysis. The location of the fire is supposed to be in a room in the middle of the building. Also, a neighborhood containing several rooms and corridors is selected around the firing room. We have used Decision Tree learning technique to classify the data and predict the fire data. Three different scenarios have been defined to evaluate the effect of distance and time in recognition of the fire pattern. KNIME has been used for implementation of pattern recognition technique in which all fire data can be classified based on the data in the firing room. The authenticity of proposed strategy is examined via the scenarios. Statistical analysis using KNIME and MATLAB show the high accuracy (at the level of 0.9 with acceptable confidence interval) in all cases. Also, the profile of the accuracy has a logical process, according to the time and distance in most of the cases.
Starting Page	130
Ending Page	137
File Size	559783
Page Count	8
File Format	PDF
e-ISBN	9781479999231
DOI	10.1109/ICT-DM.2015.7402033
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-30
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision tree learning Temperature distribution Firing Heating Buildings Fires Fire analysis Pattern recognition Decision trees Fire data classification Confidence interval
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Classifiability based omnivariate decision trees

On Classification Confidence and Ranking Using Decision Trees

Cline: new multivariate decision tree construction heuristics

Fuzzy classification trees

Visual Pattern Recognition Method Based on Optimized Parallel Coordinates

An Improved Algorithm of Decision Trees for Streaming Data Based on VFDT

A Neural Tree with Partial Incremental Learning Capability

Induction of decision trees from partially classified data using belief functions

Decision Trees for Uncertain Data

A methodology for fire data analysis based on pattern recognition towards the disaster management

Similar Documents

Classifiability based omnivariate decision trees

On Classification Confidence and Ranking Using Decision Trees

Cline: new multivariate decision tree construction heuristics

Fuzzy classification trees

Visual Pattern Recognition Method Based on Optimized Parallel Coordinates

An Improved Algorithm of Decision Trees for Streaming Data Based on VFDT

A Neural Tree with Partial Incremental Learning Capability

Induction of decision trees from partially classified data using belief functions

Decision Trees for Uncertain Data

A methodology for fire data analysis based on pattern recognition towards the disaster management