Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

Author:

Lai Can,Zeng Shucai,Guo Wei,Liu Xiaodong,Li Yongquan,Liao BoyongORCID

Abstract

Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3–19.3 and root mean squared error by 0.95–19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available.

Funder

Foundation for young talents in Zhongkai University of Agriculture and Engineering

Publisher

MDPI AG

Subject

Forestry

Reference29 articles.

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Multi-Scale Deep Learning Algorithm for Enhanced Forest Fire Danger Prediction Using Remote Sensing Images;Forests;2024-09-09

2. Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas;ISPRS International Journal of Geo-Information;2024-04-29

3. Advancements in Wildfire Detection and Prediction: An In-Depth Review;International Journal of Innovative Technology and Exploring Engineering;2024-01-30

4. IMPLEMENTATION OF THE ANFIS NEURO-FUZZY SYSTEM FOR FOREST FIRE MANAGEMENT;Scientific and analytical journal «Vestnik Saint-Petersburg university of State fire service of EMERCOM of Russia»;2023-12-30

5. A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction;2023 IEEE International Conference on Big Data (BigData);2023-12-15

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3