Machine learning for wildfire classification: Exploring blackbox, eXplainable, symbolic, and SMOTE methods-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Machine learning for wildfire classification: Exploring blackbox, eXplainable, symbolic, and SMOTE methods

Published:2022-09 Issue:3 Volume:2 Page:154-165
ISSN:2666-5921
Container-title:Natural Hazards Research
language:en
Short-container-title:Natural Hazards Research

Author:

Al-Bashiti Mohammad Khaled,Naser M.Z.^ORCID

Publisher

Elsevier BV

Subject

General Medicine

Reference79 articles.

1. A comparison of statistical downscaling methods suited for wildfire applications;Abatzoglou;Int. J. Climatol.,2012

2. Minimal ensemble based on subset selection using ECG to diagnose categories of CAN;Abawajy;Comput. Methods Progr. Biomed.,2018

3. Multi expression programming: a new approach to formulation of soil classification;Alavi;Eng. Comput.,2010

4. On the trajectories of embers initially elevated or lofted by small scale ground fire plumes in high winds;Anthenien;Fire Saf. J.,2006

Cited by 13 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. Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region;Earth Science Informatics;2024-09-06

2. Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI);Science of The Total Environment;2024-03

3. Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures;Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure;2024

4. Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms;Fire;2023-11-17

5. Applications and challenges of artificial intelligence in the field of disaster prevention, reduction, and relief;Natural Hazards Research;2023-11

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