Publications

Submitted

1. Under Rev.

   Value of probabilistic spatiotemporal urban temperature forecasts for building energy purchases in day-ahead market

   Choi, Byeongseong, Bergés, Mario, and Pozzi, Matteo

   Submitted on July 25 2025

Journal

1. Accepted

   Optimal sensor placement in low-cost PM2.5 sensor networks using value of information and multimodal data fusion

   Choi, Byeongseong, and Hummel, Michelle

   Environmental Research Letters (Accepted on December 10) 2025

   doi url
2. Published

   Seismic Structural Response and Loss Estimation for Dense Urban Districts Using Neural Network Parameterized Gaussian Process

   Choi, Byeongseong, Yi, Sang-ri, and Kim, Taeyong

   Earthquake Engineering & Structural Dynamics 2025

   Abstract doi url

   Earthquakes pose a major threat to urban areas, causing fatalities, injuries, and significant economic losses. This study proposes a Gaussian process parametrized by deep neural networks (DNN–GP) as an efficient surrogate for assessing seismic losses of building structures at a regional scale. The framework constructs a GP surrogate using three DNN models that predict the mean, standard deviation, and correlation of structural responses. These models are trained on a seismic demand database developed from a large number of dynamic analyses of structural systems, composed of nonlinear single-degree-of-freedom systems subjected to ground motions with various scaling factors. The DNN–GP model targets assessing an aggregated loss over a cluster of buildings within a limited spatial domain that are subjected to identical ground motion. A key feature of the proposed method is its ability to refine initial estimates from the component DNN models by incorporating reference points, localized nonlinear responses obtained from a selected subset of structures within the considered building portfolio. This study demonstrates the effectiveness of the surrogate model through two numerical studies: (1) prediction of seismic responses for a set of structural systems, and (2) estimation of seismic losses for a building cluster. The results show that the DNN–GP significantly reduces the need for exhaustive nonlinear simulations while maintaining accuracy and quantifying prediction uncertainty. This enables rapid, simulation-informed decision-making using the surrogate model for managing seismic risk and enhancing the resilience of urban infrastructure.

3. Published

   Cross-entropy-based adaptive concurrent importance sampling for probabilistic seismic loss analysis of spatially distributed multi-state systems

   Choi, Byeongseong, and Song, Junho

   ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2026

   Abstract doi url Note

   Spatially distributed infrastructure systems are crucial assets of a community, and thus assessing their risk under seismic hazards is essential. These assessments should be system-wide, regional, and probabilistic to provide comprehensive insights for decision-makers. Monte Carlo simulation is often used owing to its straightforward application but can be computationally inefficient for rare, high-impact events. While importance sampling can improve computational efficiency by focusing on significant outcomes, it may struggle with computing multiple probabilities simultaneously as it is usually optimized for single-event scenarios. To address these issues, we propose a novel approach termed cross-entropy-based “concurrent” adaptive importance sampling (CE-CAIS). This method efficiently samples multistate systems by concurrently estimating multiple system failure probabilities using a single near-optimal importance sampling density. The effectiveness of CE-CAIS is demonstrated using the Sioux Falls traffic network example consisting of 11 system states and a large-scale regional analysis. In the latter, dimensionality reduction techniques, such as principal component analysis, and the central limit theorem are additionally employed to assess the loss of Shelby County, Tennessee, which includes 305,694 buildings. These results highlight the potential of CE-CAIS in risk assessment of complex systems, paving the way for more effective and scalable approaches to evaluating regional impacts of seismic hazards on spatially distributed infrastructure systems. This study introduces a sampling-based approach to improve the computational efficiency of seismic risk and loss assessment at both the network and regional scales. We propose a cross-entropy-based concurrent adaptive importance sampling (CE-CAIS) method, which constructs an approximate probability density using a Gaussian mixture model, enabling effective performance across multiple system states. CE-CAIS improves efficiency by concurrently estimating the probability of various system states while minimizing variance across all estimates. This makes CE-CAIS particularly effective for evaluating complex systems such as infrastructure networks and regional building losses. In this study, we apply CE-CAIS to assess the seismic risk of a traffic network using the matrix-based system reliability method and to compute exceedance probabilities of regional loss from building damage in Shelby County, Tennessee, incorporating principal component analysis and the central limit theorem. The results demonstrate that CE-CAIS can produce accurate and efficient risk estimates, positioning this method as a promising tool for large-scale seismic risk assessment.

   Notice: The Y-axis label in Fig. 10 should read "Coefficient of Variation" instead of Probability of exceedance. This correction does not affect the results and conclusions.

4. Published

   Uncertainty quantification of building energy use using a probabilistic spatiotemporal model of urban temperature

   Choi, Byeongseong, Bergés, Mario, and Pozzi, Matteo

   Energy and Buildings 2025

   Abstract doi url

   Urban temperature significantly impacts energy use in the built environment. This paper presents a model coupling approach to quantify how uncertainty in urban temperature, particularly from localized weather effects such as the urban heat island, propagates through building energy systems and affects consumption patterns. The proposed method integrates two models: (1) a probabilistic spatiotemporal model (PSTM) to forecast near-surface temperature at a regional scale with sub-city resolution and (2) a time-series electrical load forecasting model at the building scale. The load forecasting model is calibrated using simulated energy consumption data for representative commercial and residential buildings in the United States. To improve computational efficiency, we also propose combining two uncertainty quantification algorithms: the first-order reliability method and the sequential compounding method, as an alternative to sampling-based approaches. As a numerical case study, we analyze the impact of hot weather in the City of Pittsburgh on energy consumption and the sensitivity of various building types. We also investigate how urban temperatures influence high energy use across different land use types. The numerical experiments on residential building models revealed that high energy consumption was underestimated by 4.2–10.3 %, depending on the building model, when compared to analyses based on temperatures that represent the typical weather of the region. This finding highlights that temperature conditions at a representative weather reference point, e.g., an airport, may underestimate energy use in urban areas, as it fails to account for localized weather such as the urban heat island.

5. Published

   Bayesian inference of historical streamflow changes suggests further stress in the Colorado River Basin

   Lai, Yuchuan, Choi, Byeongseong, and Roy, Sujoy B.

   Journal of Hydrology: Regional Studies 2025

   Abstract doi url

   Bayesian inference is used to analyze long-term streamflow in the CRB. Key parameters describing long-term hydroclimatic changes are integrated into a state-space model (SSM). Results from Global Climate Models (GCMs), coupled with downscaling and hydrologic modeling, are used by the SSM to inform these parameters, which are then updated based on historical observations. Multidecadal streamflow projections are made using the SSM and subsequently applied to the Colorado River Simulation System (CRSS) model to examine future river system conditions. New hydrological insights for the region. The SSM projects a long-term decline (in both average and upper and lower confidence intervals) of annual streamflow in the CRB. Compared to some GCM-coupled modeling results, the SSM suggests a modest decrease of annual precipitation in the Upper Basin because of climate change (e.g., 2.3 % lower from the long-term average by 2050), a greater temperature sensitivity (reduction per unit temperature increase) of streamflow, and thus a larger streamflow decline. The observed averaged streamflow during 2000–2023 generally aligns with the decreasing trend in the SSM out-of-sample validations and is projected to become an average condition by ∼2040, along with further declines afterwards. Additional river system modeling from the CRSS suggests, despite extraordinary conservations in recent years, additional efforts may be necessary to sustain regional water supply amid climate change.

6. Published

   Spatiotemporal air quality prediction using stochastic advection–diffusion model for multimodal data fusion

   Choi, Byeongseong, and Hummel, Michelle

   Environmental Research Letters 2025

   Abstract doi url

   Particulate matter poses significant risks to respiratory and cardiovascular health. Monitoring ambient particulate matter concentrations can provide information on potential exposures and inform mitigation strategies, but ground-based measurements are sparse. Data fusion approaches that integrate data from multiple sources can complement existing observation networks and reveal insights that single-sensor data might miss to better manage pollutant exposure risks. However, data fusion approaches face multiple challenges, including incompatible measurement units, varying data resolutions, and differing levels of uncertainty. As a result, the optimal method for data fusion remains an open question. Here, we propose a probabilistic spatiotemporal model, based on the stochastic advection–diffusion (SAD) equation, as a data fusion method to process multimodal air quality data to predict hourly concentrations of fine particulate matter (PM2.5). We employ a variational inference method to calibrate the probabilistic model using ground-level observations and the numerical output of two simulation models. We then evaluate the prediction performance of our model for two scenarios: (1) incorporating simulation outputs and ground-level observations from sparse regulatory-grade stations and (2) using ground-level observations from both low-cost and regulatory-grade stations. For the first scenario, the data fusion method reduces prediction error by 14% compared to the nearest regulatory-grade air monitor located 20 km away. For the second scenario, error is reduced by 40% compared to the nearest regulatory-grade monitor and 11% compared to the nearest low-cost sensor located approximately 1 km away. The model captures 78% of observed data within a 75% confidence interval across both scenarios, demonstrating its ability to accurately represent uncertainty. Our findings demonstrate that the proposed SAD model can effectively integrate multimodal data to provide improved prediction of particulate matter concentrations at high spatial resolution. Model outputs can inform individual and community-level decision-making to mitigate air pollutant exposures.

7. Published

   Short-term probabilistic forecasting of meso-scale near-surface urban temperature fields

   Choi, Byeongseong, Bergés, Mario, Bou-Zeid, Elie, and Pozzi, Matteo

   Environmental Modelling & Software 2021

   Abstract doi url

   This paper introduces a probabilistic approach to spatio-temporal high resolution meso-scale modeling of near-surface temperature and applies it to regions of dimension about 150∼200 km, with 1 km grid spacing and 30-min interval. Our probabilistic approach, based on linear Gaussian models and dimensionality reduction, can accurately forecast short-term temperature fields and serve as a computationally less expensive alternative to physics-based models that necessitate high-performance computing. The probabilistic models here are calibrated from simulations of a physics-based model, the Princeton Urban Canopy Model, coupled to the Weather Research and Forecasting Model (WRF-PUCM). We assess the performance of the calibrated models to forecast short-term near-surface temperature in various cases. In the numerical campaign, our models achieve 0.97–1.13 °C root mean squared error (RMSE) for 24-hours ahead forecast; generating three days of forecast takes between 20 and 170 sec on a single processor (Intel Xeon E5-2690 v4@2.60GHz). Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost.

Webapp

1. Dashboard

   Smartcoast Dashboard

   Choi, Byeongseong

   2025

   url

Data

1. Data

   Non-linear responses of SDOF systems under PEER ground motions with scale factors

   Choi, Byeongseong, Kim, Taeyong, and Yi, Sang-ri

   2025

   doi
2. Data

   SHADE 2021: Meso-scale 2m air temperature around New York City and Pittsburgh

   Choi, Byeongseong, Bergés, Mario, Bou-Zeid, Elie, and Pozzi, Matteo

   2021

   doi

Conference and Workshop

1. Conference

   Enhancing Low-Cost Sensor Networks through Multimodal Data Fusion: Application of a Probabilistic Spatiotemporal Air Quality Model

   Hummel, Michelle, and Choi, Byeongseong

   2025
2. Conference

   A community-led approach to environmental monitoring and adaptive capacity building in the coastal bend region of texas, usa

   Hummel, Michelle, Jenewein, Oswald, Bezboruah, Karabi, Liu, Yonghe, Masten, Kathryn, Choi, Byeongseong, and Sakalker, Amruta

   EGU General Assembly Conference Abstracts 2024
3. Conference

   Improving the Accuracy of a Low-Cost Environmental Monitoring System Through Application of a Stochastic Advection-Diffusion Model

   Choi, Byeongseong, and Hummel, Michelle

   AGU Fall Meeting Abstracts 2023
4. Conference

   Spatio-temporal modelling for meso-scale surface temperature

   Choi, Byeongseong, Bergés, Mario, and Pozzi, Matteo

   ICOSSAR 2021-2022,13th International Conference on Structural Safety & Reliability, 13-17 September 2022, Shanghai, China 2022
5. Conference

   Short-term probabilistic heat-induced risk assessment on urban scale energy system

   Choi, Byeongseong, Bergés, Mario, and Pozzi, Matteo

   Engineering Mechanics Institute Conference, Johns Hopkins University, Baltimore, MD, May 31-June 3, 2022 2022
6. Conference

   Data-driven short-term prediction of meso-scale surface temperature

   Choi, Byeongseong, Pozzi, Matteo, Bergés, Mario, and Bou-Zeid, Elie

   Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference, Virtual, May 25-28, 2021 2021
7. Conference

   Probabilistic approaches for surrogate modeling with high-dimensional data to predict short-term meso-scale surface temperature

   Choi, Byeongseong, Bergés, Mario, Bou-Zeid, Elie, and Pozzi, Matteo

   American Geophysical Union Fall Meeting 2020. Virtual, December 15, 2020 2020
8. Workshop

   Heat risk assessment using surrogate model for meso-scale surface temperature

   Choi, Byeongseong, Pozzi, Matteo, and Bergés, Mario

   Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing System, Virtual, December 12, 2020 2020
9. Conference

   Developing time-variant filter for meso-scale surface temperature prediction

   Choi, Byeongseong, Pozzi, Matteo, Bergés, Mario, and Bou-Zeid, Elie

   International Association for Bridge and Structural Engineering (IABSE) Conference 2020, Seoul (remote), November 9-10, 2020 2020
10. Conference

    A non-stationary surrogate model for meso-scale surface temperature with extracted diurnal spatial patterns

    Choi, Byeongseong, Pozzi, Matteo, Bergés, Mario, and Bou-Zeid, Elie

    American Geophysical Union Fall Meeting 2019. San Francisco, December 10, 2019 2019
11. Conference

    Efficient earthquake loss estimation by cross-entropy-based adaptive importance sampling

    Choi, Byeongseong, Byun, Ji-Eun, and Song, Junho

    12th International Conference on Structural Safety and Reliability, August 6-10, Vienna, Austria 2017
12. Workshop

    Novel sampling methods for seismic impact analysis of urban communities and complex infrastructure systems

    Choi, Byeongseong, and Song, Junho

    IFAT 2017 Third International Forum on Advanced Technologies, March 9-11, Hualien, Taiwan 2017
13. Conference

    Cross-entropy-based adaptive importance sampling for probabilistic seismic risk assess-ment of lifeline networks considering spatial correlation

    Choi, Byeongseong, and Song, Junho

    Urban Transitions Global Summit 2016, September 5-9, Shanghai 2016
14. Conference

    Dimension reduction techniques and efficient sampling method for regional seismic loss assessment

    Choi, Byeongseong, and Song, Junho

    Korean Society of Civil Engineers 2016 Convention, October 19-21, Jeju, Korea 2016
15. Conference

    Probabilistic seismic risk assessment of lifeline networks using cross-entropy-based adaptive importance sampling

    Choi, Byeongseong, Song, Junho, and Chi, Seokho

    Probabilistic Safety Assessment and Management 13, October 2-7, Seoul, Korea 2016
16. Workshop

    Reliability analysis of complex infrastructure systems using cross-entropy-based adaptive importance sampling

    Song, Junho, and Choi, Byeongseong

    Workshop on Resilience of Urban Infrastructure, April 24-26, Toronto, Canada 2016
17. Conference

    Seismic risk assessment for traffic network using adaptive sampling

    Choi, Byeongseong, and Song, Junho

    2016 Conference of the Earthquake Engineering Society of Korea, March 18, Suwon, Korea 2016
18. Conference

    Probabilistic loss assessment of earthquakes in Korea using open-source software platforms

    Choi, Eujeong, Choi, Byeongseong, and Song, Junho

    2015 Conference of the Earthquake Engineering Society of Korea, March 18, Suwon, Korea 2015

Thesis

1. Ph.D.

   Urban temperature and electricity demand: probabilistic modeling and adaptive decision-making

   Choi, Byeongseong

   Civil and Environmental Engineering, School of Engineering, Carnegie Mellon University 2023

   url
2. M.S.

   Efficient simulation-based approaches for community-level probabilistic seismic risk assessment

   Choi, Byeongseong

   Civil and Environmental Engineering, School of Engineering, Seoul National University 2017

   url