Assessment of Direct Economic Losses from Tropical Cyclones Based on Explainable Artificial Intelligence(XAI)
LIU Shuxian
LIU Yang
YANG Kun
ZHANG Lisheng
ZHANG Yuanda
Abstract：Explainable artificial intelligence(XAI)is increasingly recognized as a prominent development direction in the field of artificial intelligence,both in research and practical applications.This technology is actively employed to clarify how models arrive at predictions and decisions,and it holds significant value in the assessment of meteorological disasters.Within this context,this study aimed to utilize machine learning algorithms to evaluate the direct economic losses resulting from tropical cyclones(TC).Additionally,it employed XAI methods,specifically Shapley additive explanations(SHAP),to analyze the influence and contribution of feature variables on model predictions from global and local perspectives.The findings of this study consistently demonstrate that the random forest(RF)model outperformed the LightGBM model in predicting economic losses from TCs.Compared to LightGBM,the RF model achieved lower values for root mean square error(RMSE)at 23.6,mean absolute error(MAE)at 11.1,and a higher coefficient of determination(R2)at 0.9.Upon closer examination of the contribution analysis concerning feature variables,it becomes evident that hazard factor indicators played a more prominent role in predicting TC economic losses than exposure and vulnerability indicators,along with disaster risk reduction capacity indicators.Specifically,the top three contributors were identified as maximum wind speed(H3),maximum daily rainfall(H1),and the proportion of rainfall stations(H2).Among these,maximum wind speed(H3)stood out with a notably higher contribution than other indicators,signifying its pivotal importance in assessing economic losses from TCs.In a more specific context,instances where the maximum wind speed(H3)exceeded 45 m·s-1,maximum daily rainfall(H1)surpassed 250 mm,and the proportion of rainfall stations(H2)exceeded 30%,were observed to significantly enhance the accuracy of TC-induced economic loss predictions,as indicated by their significantly higher SHAP values.Overall,the advancements in XAI,combined with the effective application of ML algorithms,rendered invaluable insights into accurately assessing economic losses resulting from tropical cyclones.These insights are instrumental in informing decision-makers and policy planners in developing effective disaster risk management strategies.
Keywords：tropical cyclonesdirect economic lossesmachine learningexplainable artificial intelligence(XAI)Shapley additive explanations(SHAP)
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 943-953 )