首页|Artificial Intelligence:A new era for spatial modelling and interpreting climate-induced hazard assessment

Artificial Intelligence:A new era for spatial modelling and interpreting climate-induced hazard assessment

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The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent years.The field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms,the availability of large quantities of data,and the increase in compu-tational capacity.The enhancement in algorithms can be largely attributed to the elevated complexity of the network architecture and the heightened level of abstraction found in the network's later layers.As a result,AI models lack transparency and accountability,often being dubbed as"black box"models.Explainable AI(XAI)is emerging as a solution to make AI models more transparent,especially in domains where transparency is essential.Much discussion surrounds the use of XAI for diverse purposes,as researchers explore its applications across various domains.With the growing body of research papers on XAI case studies,it has become increasingly important to address existing gaps in the literature.The current literature lacks a comprehensive understanding of the capabilities,limitations,and practical implications of XAI.This study provides a comprehensive overview of what constitutes XAI,how it is being used and potential applications in hydrometeorological natural hazards.It aims to serve as a useful reference for researchers,practitioners,and stakeholders who are currently using or intending to adopt XAI,thereby contributing to the advancements for wider acceptance of XAI in the future.

Artificial IntelligenceExplainable AI(XAI)Climate changeSpatial modellingNatural hazards

Abhirup Dikshit、Biswajeet Pradhan、Sahar S.Matin、Ghassan Beydoun、M.Santosh、Hyuck-Jin Park、Khairul Nizam Abdul Maulud

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Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),School of Civil & Environmental Engineering,Faculty of Engineering and IT,University of Technology Sydney,NSW 2007,Australia

Earth Observation Center,Institute of Climate Change,Universiti Kebangsaan Malaysia,43600 UKM Bangi,Selangor,Malaysia

Department of Energy and Mineral Resources Engineering,Sejong University,Choongmu-gwan,209 Neungdong-ro,Gwangjin-gu,Seoul 05006,Republic of Korea

School of Earth Sciences and Resources,China University of Geosciences Beijing,Xueyuan Road,Beijing 100083,China

Department of Earth Sciences,University of Adelaide,Adelaide,South Australia,Australia

Faculty of Science,Kochi University,Kochi 780-8520,Japan

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Centre for Advanced Modelling and Geospatial Information Systems,Faculty of Engineering and Information Technology,UniversitIRTP scholarship funded by the Department of Education and Training,Govt.of Australia

2024

10.1016/j.gsf.2024.101815

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(4)
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