Maximizing the forecasting skill of an ensemble model

Author:

Herrmann Marcus1ORCID,Marzocchi Warner1ORCID

Affiliation:

1. Dipartimento di Scienze della Terra , dell'Ambiente e delle Risorse; Università degli studi di Napoli ‘Federico II’, L3 - 2.23, Edificio 10, Complesso Universitario di Monte Sant'Angelo, Via Cinthia 21, 80126 Napoli , Italy

Abstract

SUMMARYAn ensemble model integrates forecasts of different models (or different parametrizations of the same model) into one single ensemble forecast. This procedure has different names in the literature and is approached through different philosophies in theory and practice. Previous approaches often weighted forecasts equally or according to their individual skill. Here we present a more meaningful strategy by obtaining weights that maximize the skill of the ensemble. The procedure is based on a multivariate logistic regression and exposes some level of flexibility to emphasize different aspects of seismicity and address different end users. We apply the ensemble strategy to the operational earthquake forecasting system in Italy and demonstrate its superior skill over the best individual forecast model with statistical significance. In particular, we highlight that the skill improves when exploiting the flexibility of fitting the ensemble, for example using only recent and not the entire historical data.

Funder

Horizon 2020 Framework Programme

Publisher

Oxford University Press (OUP)

Subject

Geochemistry and Petrology,Geophysics

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Developing, Testing, and Communicating Earthquake Forecasts: Current Practices and Future Directions;Reviews of Geophysics;2024-08-13

2. A Software Tool for Hybrid Earthquake Forecasting in New Zealand;Seismological Research Letters;2024-07-26

3. suiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland;Bulletin of the Seismological Society of America;2024-05-24

4. Earthquake forecasting from paleoseismic records;Nature Communications;2024-03-02

5. Comparative evaluation of point process forecasts;Annals of the Institute of Statistical Mathematics;2023-06-15

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3