The integration of large language models(LLMs)into financial applications has demonstrated remarkable potential for enhancing decision-making processes,automating operations,and delivering personalized services.Howeve...The integration of large language models(LLMs)into financial applications has demonstrated remarkable potential for enhancing decision-making processes,automating operations,and delivering personalized services.However,the high-stakes nature of financial systems demands a very high level of trustworthiness that current LLMs often fail to meet.展开更多
Storm surges in the Western North Pacific cause significant economic damage and loss of life,highlighting the need for accurate storm surge predictions.This study evaluated four storm surge models:the Global Tide and ...Storm surges in the Western North Pacific cause significant economic damage and loss of life,highlighting the need for accurate storm surge predictions.This study evaluated four storm surge models:the Global Tide and Surge Model(GTSMv3.0),ERA20C neural network(ERA20C_nn),ERA20C multiple linear regression(ERA20C_ml),and 20th Century Reanalysis multiple linear regression(20CR_ml),using data from 160 tidal stations.The results show that the ERA20C_nn model outperformed others,with the highest correlation to tide-gauge observations.The GTSMv3.0 model follows closely,although slightly less accurate.The ERA20C_ml and 20CR_ml models were less effective,especially in predicting extreme surges.The ERA20C_nn model also provided more reliable estimates for 100-year return surge levels,outperforming other models.These findings suggest that neural network-based models,particularly ERA20C_nn,are better suited for assessing coastal flood risks in the region.展开更多
文摘The integration of large language models(LLMs)into financial applications has demonstrated remarkable potential for enhancing decision-making processes,automating operations,and delivering personalized services.However,the high-stakes nature of financial systems demands a very high level of trustworthiness that current LLMs often fail to meet.
基金supported by the National Natural Science Foundation of China(Grant Nos.42176198,42176203)the National Key Research and Development Program of China(Grant No.2023YFC3008200)funding from the Taishan Scholars Program(tsqn202211252)。
文摘Storm surges in the Western North Pacific cause significant economic damage and loss of life,highlighting the need for accurate storm surge predictions.This study evaluated four storm surge models:the Global Tide and Surge Model(GTSMv3.0),ERA20C neural network(ERA20C_nn),ERA20C multiple linear regression(ERA20C_ml),and 20th Century Reanalysis multiple linear regression(20CR_ml),using data from 160 tidal stations.The results show that the ERA20C_nn model outperformed others,with the highest correlation to tide-gauge observations.The GTSMv3.0 model follows closely,although slightly less accurate.The ERA20C_ml and 20CR_ml models were less effective,especially in predicting extreme surges.The ERA20C_nn model also provided more reliable estimates for 100-year return surge levels,outperforming other models.These findings suggest that neural network-based models,particularly ERA20C_nn,are better suited for assessing coastal flood risks in the region.
