基于可解释人工智能(XAI)的热带气旋直接经济损失评估研究

Assessment of Direct Economic Losses from Tropical Cyclones Based on Explainable Artificial Intelligence (XAI)

关键词:  热带气旋  直接经济损失  机器学习  可解释人工智能  SHAP

Key words:  tropical cyclones  direct economic losses  machine learning  explainable artificial intelligence (XAI)  Shapley additive explanations (SHAP)

摘要:

可解释人工智能(eXplainable Artificial Intelligence，XAI)已经成为人工智能研究领域的重要发展方向，该技术可以帮助解释模型如何做出预测和决策，在气象灾害评估领域具有较大应用价值。本研究旨在利用机器学习算法评估热带气旋(Tropical Cyclone，TC)的直接经济损失，并采用 XAI 方法 SHAP(SHapley AdditiveexPlanations)，从全局和局部层面分析特征因素对模型预测的影响和贡献。结果表明，随机森林(RandomForest, RF)模型在均方根误差、平均绝对误差和决定系数这三个评估指标中均优于 LightGBM(Light GradientBoosting Machine)模型，指标值分别达到了 23.6、11.1 和 0.9。根据 SHAP 值，RF 模型中最重要的三个因素分别是极大风速、最大日雨量和暴雨站点比例。具体而言，当样本的极大风速值大于 45 m·s-1、最大日雨量值超过250 mm 以及暴雨站点比例高于 30% 时，往往对 TC 直接经济损失预测值产生较大的正贡献。该研究可以为决策者制定灾害风险管理策略提供有力的科学依据和理论支持。

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.