Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)

Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)

The nonlinearity nature of land subsidence and limited observations cause premature convergence in typical data assimilation methods, leading to both underestimation and miscalculation of uncertainty in model parameters and prediction. This study focuses on a promising approach, the combination of e...

Full description

Bibliographic Details
Main Authors: Kento Akitaya, Masaatsu Aichi
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Water
Subjects:
land subsidence
model parameter uncertainty
predictive uncertainty
data assimilation
evolutionary algorithm
ensemble forecast
Online Access:https://www.mdpi.com/2073-4441/16/3/423

Internet

https://www.mdpi.com/2073-4441/16/3/423

Similar Items