首页|On the use of explainable AI for susceptibility modeling:Examining the spatial pattern of SHAP values

On the use of explainable AI for susceptibility modeling:Examining the spatial pattern of SHAP values

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Hydro-morphological processes(HMP,any natural phenomenon contained within the spectrum defined between debris flows and flash floods)are globally occurring natural hazards which pose great threats to our society,leading to fatalities and economical losses.For this reason,understanding the dynamics behind HMPs is needed to aid in hazard and risk assessment.In this work,we take advantage of an explainable deep learning model to extract global and local interpretations of the HMP occurrences across the whole Chinese territory.We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values.In doing so,we can understand the model prediction on a hierarchical basis,looking at how the predictor set controls the overall susceptibility as well as doing the same at the level of the single mapping unit.Our model accurately predicts HMP occurrences with AUC values measured in a ten-fold cross-validation ranging between 0.83 and 0.86.This level of predic-tive performance attests for an excellent prediction skill.The main difference with respect to traditional statistical tools is that the latter usually lead to a clear interpretation at the expense of high performance,which is otherwise reached via machine/deep learning solutions,though at the expense of interpretation.The recent development of explainable Al is the key to combine both strengths.In this work,we explore this combination in the context of HMP susceptibility modeling.Specifically,we demonstrate the extent to which one can enter a new level of data-driven interpretation,supporting the decision-making process behind disaster risk mitigation and prevention actions.

Hydro-morphological processesSHAP mapsExplainable AIChina

Nan Wang、Hongyan Zhang、Ashok Dahal、Weiming Cheng、Min Zhao、Luigi Lombardo

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Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains,Ministry of Education,School of Geographical Sciences,Northeast Normal University,Changchun 130024,China

State Key Laboratory of Resources and Environmental Information Systems,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

University of Twente,Faculty of Geo-Information Science and Earth Observation(ITC),PO Box 217,Enschede,AE 7500,Netherlands

University of Chinese Academy of Sciences,Beijing 100049,China

Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application,Nanjing 210023,China

Collaborative Innovation Center of South China Sea Studies,Nanjing 210093,China

State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing 100875,China

Key Laboratory of Environmental Change and Natural Disaster,Beijing Normal University,Beijing 100875,China

Center for Geodata and Analysis,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China

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National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesChina Institute of Water Resources and Hydropower Research(IWHR)

422014522412022QD003

2024

10.1016/j.gsf.2024.101800

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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