• Title/Summary/Keyword: parameters estimation

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Physical Dimensions of Planet-hosting Stars

  • Bach, Kiehunn;Kang, Wonseok
    • The Bulletin of The Korean Astronomical Society
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    • v.44 no.1
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    • pp.85.1-85.1
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    • 2019

  • Accurate estimation of the masses, the ages, and the chemical abundances of host stars is crucial to understand physical characteristics of exo-planetary systems. In this study, we investigate physical dimensions of 94 planet-hosting stars based on spectroscopic observation and stellar evolutionary computation, From the high resolution echelle spectroscopy of the BOES observation, we have analysed metallicities and alpha-element enhancements of host stars. By combining recent spectro-photometric observations, stellar parameters are calibrated within the frame work of the standard stellar theory. In general, the minimum chi-square estimation can be strongly biased in cases that stellar properties rapidly changes after the terminal age main-sequence. Instead, we adopt a Bayesian statistics considering a priori distribution of stellar parameters during the rapid evolutionary phases. we determine a reliable set of stellar parameters between theoretical model grids. To overcome this statistical bias, (1) we adopt a Bayesian statistics considering a priori distribution of stellar parameters during the rapid evolutionary phases and (2) we construct the fine model grid that covers mass range ($0.2{\sim}3.0M_{\odot}$) with the mass step ${\Delta}M=0.01M_{\odot}$, metallicities Z = 0.0001 ~ 0.04, and the helium and the alpha-element enhancement. In this presentation, we introduce our calibration scheme for several hosting stars.

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Calibration and uncertainty analysis of integrated surface-subsurface model using iterative ensemble smoother for regional scale surface water-groundwater interaction modeling

  • Bisrat Ayalew Yifru;Seoro Lee;Woon Ji Park;Kyoung Jae Lim
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.287-287
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    • 2023

  • Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

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Application of Bayesian Calibration for Optimizing Biophysicochemical Reaction Kinetics Models in Water Environments and Treatment Systems: Case Studies in the Microbial Growth-decay and Flocculation Processes (베이지안 보정 기법을 활용한 생물-물리-화학적 반응 동역학 모델 최적화: 미생물 성장-사멸과 응집 동역학에 대한 사례 연구)

  • Byung Joon Lee
    • Journal of Korean Society on Water Environment
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    • v.40 no.4
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    • pp.179-194
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    • 2024

  • Biophysicochemical processes in water environments and treatment systems have been great concerns of engineers and scientists for controlling the fate and transport of contaminants. These processes are practically formulated as mathematical models written in coupled differential equations. However, because these process-based mathematical models consist of a large number of model parameters, they are complicated in analytical or numerical computation. Users need to perform substantial trials and errors to achieve the best-fit simulation to measurements, relying on arbitrary selection of fitting parameters. Therefore, this study adopted a Bayesian calibration method to estimate best-fit model parameters in a systematic way and evaluated the applicability of the calibration method to biophysicochemical processes of water environments and treatment systems. The Bayesian calibration method was applied to the microbial growth-decay kinetics and flocculation kinetics, of which experimental data were obtained with batch kinetic experiments. The Bayesian calibration method was proven to be a reasonable, effective way for best-fit parameter estimation, demonstrating not only high-quality fitness, but also sensitivity of each parameter and correlation between different parameters. This state-of-the-art method will eventually help scientists and engineers to use complex process-based mathematical models consisting of various biophysicochemical processes.

  • https://doi.org/10.15681/KSWE.2024.40.4.179 인용 PDF