Paper Title
FOREST FIRE PREDICTION USING MLP CLASSIFIER
Article Identifiers
Authors
Arun Nair , Atharva Sharma , Aashi Tiwri , Akhil Mathew
Keywords
MLP CLassifier
Abstract
This project proposes a machine learning solution for forest fire prediction. It involves data collection, preprocessing, and real-time data integration. Various machine learning algorithms are evaluated to select the best model, and evaluation metrics are used to assess its performance. The project aims to provide decision support for forest management agencies in resource allocation and preventive measures, enhancing forest fire prediction and response strategies.
Downloads
How To Cite (APA)
Arun Nair, Atharva Sharma, Aashi Tiwri, & Akhil Mathew (November-2023). FOREST FIRE PREDICTION USING MLP CLASSIFIER. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), b797-b802. https://ijnrd.org/papers/IJNRD2311200.pdf
Issue
Volume 8 Issue 11, November-2023
Pages : b797-b802
Other Publication Details
Paper Reg. ID: IJNRD_209091
Published Paper Id: IJNRD2311200
Downloads: 000121983
Research Area: Computer EngineeringÂ
Country: Nagpur, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2311200.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311200
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG
Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
Publication Timeline
Article Preview: View Full Paper
Call For Paper
IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.
The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.
Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.
How to submit the paper?
By Our website
Click Here to Submit Paper Online
Important Dates for Current issue
Paper Submission Open For: October 2025
Current Issue: Volume 10 | Issue 10 | October 2025
Impact Factor: 8.76
Last Date for Paper Submission: Till 31-Oct-2025
Notification of Review Result: Within 1-2 Days after Submitting paper.
Publication of Paper: Within 01-02 Days after Submititng documents.
Frequency: Monthly (12 issue Annually).
Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.
Subject Category: Research Area
Call for Paper: More Details
