Orlicz risks for assessing stochastic streamflow environments: a static optimization approach DOI Creative Commons
Hidekazu Yoshioka, Haruka Tomobe, Yumi Yoshioka

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 38(1), P. 233 - 250

Published: Oct. 4, 2023

Abstract This study applies novel risk measures, called Orlicz risks, to the and uncertainty evaluation of streamflow discharge as a primary driver hydrological hydraulic processes interest in civil environmental engineering. We consider mixed moving average process governing whose statistics are explicitly represented some product time-scale characterizing flow attenuation jump moment size frequency jumps. The classical risks extended so that not only upper tail but also lower one can be evaluated within single mathematical framework. Further, individually quantified tractable manner by proposed risks. Computing reduces solving pair static optimization problems solvable semi-analytically. involved dynamics consistently specifying few user-dependent parameters. associated Radon–Nikodym derivatives worst-case model uncertainties obtained byproducts. Sufficient conditions for well-posedness discussed numerical algorithms computing them presented. finally apply framework statistical analysis time series data collected at mountainous river environments.

Language: Английский

Modeling stationary, periodic, and long memory processes by superposed jump-driven processes DOI
Hidekazu Yoshioka

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 188, P. 115357 - 115357

Published: Sept. 7, 2024

Language: Английский

Citations

2
Orlicz risks for assessing stochastic streamflow environments: a static optimization approach DOI Creative Commons
Hidekazu Yoshioka, Haruka Tomobe, Yumi Yoshioka

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 38(1), P. 233 - 250

Published: Oct. 4, 2023

Abstract This study applies novel risk measures, called Orlicz risks, to the and uncertainty evaluation of streamflow discharge as a primary driver hydrological hydraulic processes interest in civil environmental engineering. We consider mixed moving average process governing whose statistics are explicitly represented some product time-scale characterizing flow attenuation jump moment size frequency jumps. The classical risks extended so that not only upper tail but also lower one can be evaluated within single mathematical framework. Further, individually quantified tractable manner by proposed risks. Computing reduces solving pair static optimization problems solvable semi-analytically. involved dynamics consistently specifying few user-dependent parameters. associated Radon–Nikodym derivatives worst-case model uncertainties obtained byproducts. Sufficient conditions for well-posedness discussed numerical algorithms computing them presented. finally apply framework statistical analysis time series data collected at mountainous river environments.

Language: Английский

Citations

3