Article
Engineering, Manufacturing
P. Honarmandi, R. Seede, L. Xue, D. Shoukr, P. Morcos, B. Zhang, C. Zhang, A. Elwany, I. Karaman, R. Arroyave
Summary: The Eagar-Tsai (E-T) model in the context of 3D printing was studied systematically from an uncertainty quantification/propagation (UQ/UP) perspective. Model parameters were calibrated against experimental data using Markov Chain Monte Carlo (MCMC) sampling, and posterior distributions of parameter values were propagated. It was found that discrepancies between predicted and measured melt pool depths existed under keyholing conditions, but a physics-based correction improved agreement with experiments without increasing model complexity significantly.
ADDITIVE MANUFACTURING
(2021)
Article
Engineering, Environmental
Marco Bacci, Jonas Sukys, Peter Reichert, Simone Ulzega, Carlo Albert
Summary: Due to limited knowledge about complex environmental systems, predicting their behavior under different scenarios or decision alternatives is uncertain. Considering, quantifying, and communicating this uncertainty is important for societal decisions. Stochastic models are often necessary to adequately describe uncertainty, but calibrating these models presents methodological and numerical challenges. To address this, we compare three numerical approaches and find that their performance is comparable for analyzing a stochastic hydrological model with hydrological data, suggesting that generality and practical considerations can guide technique choice for specific applications.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Materials Science, Multidisciplinary
Adam P. Generale, Richard B. Hall, Robert A. Brockman, V. Roshan Joseph, George Jefferson, Larry Zawada, Jennifer Pierce, Surya R. Kalidindi
Summary: This study demonstrates the successful application of Bayesian inference for simultaneous estimation of eleven material parameters of a viscous multimode CDM model, providing uncertainty estimates and principled decision making, applicable to mechanical models with high-dimensional parameter sets.
MECHANICS OF MATERIALS
(2022)
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Karthik Reddy Lyathakula, Fuh-Gwo Yuan
Summary: The paper presents an efficient and robust probabilistic fatigue life prediction framework for adhesively bonded joints, calibrating the fatigue life model with experimental data and utilizing probabilistic assessment and neural networks for prediction. This framework allows rapid simulation of fatigue degradation and quantification of uncertainties for probabilistic fatigue life prediction in various joint configurations.
INTERNATIONAL JOURNAL OF FATIGUE
(2021)
Article
Engineering, Civil
Jia-Hua Yang, Heung-Fai Lam, Yong-Hui An
Summary: The paper proposes a new two-phase adaptive MCMC method to address the problem of determining the posterior probability density function (PDF) in Bayesian model updating. By using a parameter-space search algorithm and a weighted MCMC algorithm, samples in the regions of high probability can be generated adaptively without going through computationally demanding multiple levels.
ENGINEERING STRUCTURES
(2022)
Review
Green & Sustainable Science & Technology
D. Hou, I. G. Hassan, L. Wang
Summary: Building Energy Model (BEM) calibration is crucial for accuracy, with recent focus on stochastic Bayesian inference calibration. However, confusion remains regarding theory, strengths, limitations, and implementations. Selecting appropriate mathematical models and tools poses a challenge.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Construction & Building Technology
Hai-Bin Huang, Wei Zhang, Zhi-Guo Sun, Dong-Sheng Wang
Summary: In this study, a probabilistic approach is proposed to improve the predictive accuracy and uncertainty quantification of existing deterministic bond strength models for EB FRP-to-concrete joints. Through evaluation and calibration using an experimental database, the proposed probabilistic models are capable of accurately predicting the bond strength of EB FRP-to-concrete joints and quantifying the corresponding uncertainties.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Environmental Sciences
Babak Jamhiri, Yongfu Xu, Fazal E. Jalal
Summary: This study investigated different cracking prediction models and performed sensitivity analysis to evaluate the uncertainties of the models and parameters. The findings suggest that the linear elastoplastic model provides reasonable predictions, while soil parameter variations play an important role. Furthermore, the findings of this study can improve the decision-making processes for expansive soil stabilization by considering a variety of environmental conditional probabilities.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Mathematics, Applied
Nikolaj T. Mucke, Benjamin Sanderse, Sander M. Bohte, Cornelis W. Oosterlee
Summary: In the context of solving inverse problems in physics using Bayesian inference, a new approach called Markov Chain Generative Adversarial Neural Network (MCGAN) is proposed to reduce computational costs. By training a GAN to sample from a low-dimensional latent space and incorporating it into a Markov Chain Monte Carlo method, efficient sampling from the posterior distribution is achieved, replacing the need for high-dimensional priors and expensive forward mappings. The proposed methodology converges to the true posterior in Wasserstein-1 distance and sampling from the latent space is weakly equivalent to sampling in the high-dimensional space.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2023)
Article
Engineering, Industrial
Zeyu Wang, Abdollah Shafieezadeh
Summary: This paper presents a new approach to overcome the computational cost problem of Bayesian updating for complex computational models. It decomposes the updating problem into a set of sub-reliability problems with uncertain failure thresholds, enabling precise identification of intermediate failure thresholds and training of surrogate models. The proposed method reduces computational costs significantly while maintaining high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Mechanics
Lingyu Yue, Marie-Claude Heuzey, Jonathan Jalbert, Martin Levesque
Summary: A framework based on Bayesian inference is proposed in this study to identify the minimum parameter set in linear viscoelastic constitutive theories. Experimental validation demonstrates the robustness and adequacy of this method.
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
(2021)
Article
Engineering, Mechanical
C. Argyris, C. Papadimitriou, G. Samaey, G. Lombaert
Summary: A Bayesian framework for optimal sensor placement based on model optimization is presented to minimize uncertainty in predicting a specific quantity of interest. Emphasizing prediction inference over parameter inference, the method aims to reduce uncertainty in key parameters for accurate predictions. By using the determinant to measure uncertainty and evaluating covariance matrices through Monte Carlo sampling, the approach differs from traditional methods and is more suitable for prediction inference.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Psychology, Multidisciplinary
Jiwei Zhang, Jing Lu, Jing Yang, Zhaoyuan Zhang, Shanshan Sun
Summary: The MMS-DINA model is a mixture cognitive diagnosis model developed to investigate individual differences in response category selection in multiple-strategy items. This model system allows for multiple strategies in problem solving and the association of different strategies with different levels of difficulty.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Engineering, Environmental
Bing Bai, Fei Dong, Wenqi Peng, Xiaobo Liu
Summary: This paper proposes a novel Bayesian-based method for calibrating water quality model parameters to improve efficiency and accuracy while avoiding local optima and parameter redundancy. By converting the calibration problem into a posterior probability function sampling problem and using the Markov Chain Monte Carlo algorithm, the parameter calibration is achieved. The results show that the method can achieve calibration with less than 10% mean relative error, indicating its effectiveness in water quality modeling.
WATER ENVIRONMENT RESEARCH
(2023)
Article
Engineering, Civil
Dilhani Jayathilake, Tyler Smith
Summary: This study investigates the sensitivity of a widely used conceptual rainfall-runoff model to systematic errors in potential evapotranspiration (PET) inputs for 57 US catchments. The results show that energy-limited catchments are more sensitive to PET errors, and the PET error threshold decreases along the water- to energy-limited continuum. Additionally, negatively biased PET data can cause catchments to shift towards energy limitation, resulting in higher model sensitivity.
JOURNAL OF HYDROLOGIC ENGINEERING
(2022)
Article
Environmental Sciences
Hae Na Yoon, Lucy Marshall, Ashish Sharma, Seokhyeon Kim
Summary: This study presents a novel approach for modeling streamflow in ungauged catchments using remotely sensed data. The approach utilizes the satellite-derived calibration ratio-measurement (C/M ratio) as a direct measurement of streamflow. The study demonstrates the effectiveness of the approach for three Australian Hydrologic Reference Stations and suggests significant improvements over traditional approaches.
WATER RESOURCES RESEARCH
(2022)
Article
Water Resources
Saman Razavi, David M. Hannah, Amin Elshorbagy, Sujay Kumar, Lucy Marshall, Dimitri P. Solomatine, Amin Dezfuli, Mojtaba Sadegh, James Famiglietti
Summary: Machine learning applications in Earth and environmental sciences have evolved separately from traditional process-based modeling paradigms. Overcoming cultural barriers and exploring the strengths and weaknesses of both approaches are essential for developing a coevolutionary approach to model building.
HYDROLOGICAL PROCESSES
(2022)
Article
Water Resources
Ahmad Hasan Nury, Ashish Sharma, Lucy Marshall, Ian Cordery
Summary: Understanding the hydrological processes in the Tibetan Plateau is crucial due to the demand for freshwater downstream. However, the limited information makes it challenging to develop a hydrological model that characterizes future streamflow. This study proposes a flexible conceptual hydrological model based on remote sensing data, which can simulate dynamically varying snow cover fraction, snow water equivalent, and streamflow.
HYDROLOGICAL SCIENCES JOURNAL
(2022)
Article
Meteorology & Atmospheric Sciences
Ahmad Hasan Nury, Ashish Sharma, Raj Mehrotra, Lucy Marshall, Ian Cordery
Summary: This study examines the impact of climate change on snowpack change over the Tibetan Plateau. The results show that the region will experience longer periods without snow and a decrease in the proportion of snowy days in the future. Additionally, there will be a decrease in snow water equivalent due to warming temperatures and changes in precipitation.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Computer Science, Interdisciplinary Applications
Tian Guo, Yaoze Liu, Gang Shao, Bernard A. Engel, Ashish Sharma, Lucy A. Marshall, Dennis C. Flanagan, Raj Cibin, Carlington W. Wallace, Kaiguang Zhao, Dongyang Ren, Johann Vera Mercado, Mohamed A. Aboelnour
Summary: This study generated and evaluated probabilistic hydrologic and water quality predictions at 18 locations in the U.S., and found the best predictive uncertainties using a residual-based modeling approach. The ensemble average of hydrologic and water quality simulations better represented the predictive uncertainty compared to a single realization of simulations, especially for large watersheds. The study recommends various methods to improve the robustness and uncertainty of hydrologic and water quality predictions.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Water Resources
Shaun S. H. Kim, Justin D. Hughes, Lucy A. Marshall, Cuan Petheram, Ashish Sharma, Jai Vaze, Russell S. Crosbie
Summary: A new river loss model was developed to better represent the exchange of water between rivers and groundwater. The model performed better than a benchmark model in a case study in Cooper Creek, Australia, and provides river state and flux terms that are not typically available in basin-scale models. This model has the potential to be valuable for calibration and validation using alternative observed data types.
HYDROLOGICAL PROCESSES
(2022)
Article
Environmental Sciences
C. M. Stephens, H. T. Pham, L. A. Marshall, F. M. Johnson
Summary: There is interest in using satellite-derived rainfall products for water management in areas with limited data. This study found that a flexible hydrologic model (GR4J) was able to filter errors in rainfall magnitude and variance, making it a useful alternative when bias correction data is unavailable. However, the model had difficulty compensating for errors in rainfall occurrence. Increasing the spatial and temporal resolution of satellite observations could enhance satellite-derived precipitation for hydrologic modeling.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
K. Waddington, D. Khojasteh, L. Marshall, D. Rayner, W. Glamore
Summary: The development of low elevation coastal zones often involves reclamation and drainage systems. However, sea level rise and tidal changes can affect drainage and necessitate changes to existing land uses.
WATER RESOURCES RESEARCH
(2022)
Article
Water Resources
Clare M. Stephens, Michelle Ho, Susanne Schmeidl, Hung T. Pham, Andrew P. Dansie, Gregory L. Leslie, Lucy A. Marshall
Summary: Water operator partnerships (WOPs) have gained popularity in pursuit of SDG 6. This study found that communication and relationship-building are key success factors for achieving desired outcomes, while broader institutional learning becomes increasingly important as participants gain experience. Expanding partnerships to involve governance and policy organizations and collaboratively implementing upgrades could enhance future programmes.
INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT
(2023)
Article
Environmental Sciences
C. M. Stephens, L. A. Marshall, F. M. Johnson, H. Ajami, L. Lin, L. E. Band
Summary: Future shifts in rainfall, temperature and carbon dioxide will have varying impacts on hydrologic and ecosystem behavior, with spatial heterogeneity and important differences in riparian zones. Models need to consider spatial heterogeneity, key ecosystem-driving dynamics and lateral transport to accurately predict ecohydrologic changes in catchments.
WATER RESOURCES RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Arpit Kapoor, Anshul Negi, Lucy Marshall, Rohitash Chandra
Summary: Cyclone track forecasting is a critical climate science problem, and machine learning methods, especially recurrent neural networks (RNNs), have shown promise in this field. However, these methods often lack uncertainty quantification. This paper proposes variational RNNs, which approximate the posterior distribution of parameters by minimizing the KL-divergence loss, for cyclone track and intensity prediction. The results demonstrate that variational RNNs provide a good approximation with uncertainty quantification while maintaining prediction accuracy.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Engineering, Civil
N. Harvey, L. Marshall, R. W. Vervoort
Summary: Calibrating a hydrological model using multiple independent data sets can improve parameter estimation, but often leads to indistinguishable performance among parameter sets. This study investigates the performance of Pareto optimal solutions during model validation and the tradeoffs between objective functions during calibration. The ecohydrological model used focuses on a forested Australian catchment and simulates leaf area index, evapotranspiration, and streamflow. The results show that the performance deteriorated between calibration and validation, with no clear optimal parameter set identified from the Pareto set.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Environmental
Wenhui Wu, Behzad Jamali, Kefeng Zhang, Lucy Marshall, Ana Deletic
Summary: In this study, a new Water Sensitive Urban Design (WSUD) spatial prioritisation framework was developed using global sensitivity analysis (GSA) to identify priority subcatchments for effective flood mitigation. The framework combines the Urban Biophysical Environments and Technologies Simulator (UrbanBEATS) and the U.S. EPA Storm Water Management Model (SWMM) to simulate flooding in an urbanised catchment. The GSA was used to vary the effective imperviousness of subcatchments and prioritize their influence on catchment flooding. The effectiveness of the framework was validated by comparing different WSUD spatial distribution scenarios in a Sydney catchment. The results showed that implementing WSUD in high priority subcatchments achieved the largest flood volume reduction, followed by medium priority subcatchments and catchmentwide implementation.
Article
Water Resources
Xia Wu, Lucy Marshall, Ashish Sharma
Summary: This study introduces the BEAR method for handling multiple sources of observational errors in water quality models to accurately estimate model parameters, which has been shown to successfully quantify sources of observational errors. Considering observational errors in both model inputs and outputs, rather than just inputs or outputs, can improve parameter calibration and error characterization.
HYDROLOGICAL PROCESSES
(2022)
Article
Computer Science, Interdisciplinary Applications
Jeffrey Wade, Christa Kelleher, Barret L. Kurylyk
Summary: This study developed a physically-based water temperature model coupled with the National Water Model (NWM) to assess the potential for water temperature prediction to be incorporated into the NWM at the continental scale. By evaluating different model configurations of increasing complexity, the study successfully simulated hourly water temperatures in the forested headwaters of H.J. Andrews Experimental Forest in Oregon, USA, providing a basis for integrating water temperature simulation with predictions from the NWM.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaun SH. Kim, Lucy A. Marshall, Justin D. Hughes, Lynn Seo, Julien Lerat, Ashish Sharma, Jai Vaze
Summary: A major challenge in hydrologic modelling is producing reliable uncertainty estimates outside of calibration periods. This research addresses the challenge by improving model structures and error models to more reliably estimate uncertainty. The combination of the RBS model and SPUE produces statistically reliable predictions and shows better matching performance in tests.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Juan Pedro Carbonell-Rivera, Javier Estornell, Luis Angel Ruiz, Pablo Crespo-Peremarch, Jaime Almonacid-Caballer
Summary: This study presents Class3Dp, a software for classifying vegetation species in colored point clouds. The software utilizes geometric, spectral, and neighborhood features along with machine learning methods to classify the point cloud, allowing for the recognition of species composition in an ecosystem.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhi Li, Daniel Caviedes-Voullieme, Ilhan Oezgen-Xian, Simin Jiang, Na Zheng
Summary: The optimal strategy for solving the Richards equation numerically depends on the specific problem, particularly when using GPUs. This study investigates the parallel performance of four numerical schemes on both CPUs and GPUs. The results show that the scaling of Richards solvers on GPUs is influenced by various factors. Compared to CPUs, parallel simulations on GPUs exhibit significant variation in scaling across different code sections, with poorly-scaled components potentially impacting overall performance. Nonetheless, using GPUs can greatly enhance computational speed, especially for large-scale problems.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ludovic Cassan, Leo Pujol, Paul Lonca, Romain Guibert, Helene Roux, Olivier Mercier, Dominique Courret, Sylvain Richard, Pierre Horgue
Summary: Methods and algorithms for measuring stream surface velocities have been continuously developed over the past five years to adapt to specific flow typologies. The free software ANDROMEDE allows easy use and comparison of these methods with image processing capabilities designed for measurements in natural environments and with unmanned aerial vehicles. The validation of the integrated algorithms is presented on three case studies that represent the targeted applications: the study of currents for eco-hydraulics, the measurement of low water flows and the diagnosis of hydraulic structures. The field measurements are in very good agreement with the optical measurements and demonstrate the usefulness of the tool for rapid flow diagnosis for all the intended applications.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Mariia Kozlova, Robert J. Moss, Julian Scott Yeomans, Jef Caers
Summary: This paper introduces a framework for quantitative sensitivity analysis using the SimDec visualization method, and tests its effectiveness on decision-making problems. The framework captures critical information in the presence of heterogeneous effects, and enhances its practicality by introducing a formal definition and classification of heterogeneous effects.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chad R. Palmer, Denis Valle, Edward V. Camp, Wendy-Lin Bartels, Martha C. Monroe
Summary: Simulation games have been used in natural resource management for education and communication purposes, but not for data collection. This research introduces a new design process which involves stakeholders and emphasizes usability, relevance, and credibility testing criteria. The result is a finalized simulation game for future research.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Tao Wang, Chenming Zhang, Ye Ma, Harald Hofmann, Congrui Li, Zicheng Zhao
Summary: This study used numerical modeling to investigate the formation process of iron curtains under different freshwater and seawater conditions. It was found that Fe(OH)3 accumulates on the freshwater side, while the precipitation is inhibited on the seaward side due to high H+ concentrations. These findings enhance our understanding of iron transformation and distribution in subterranean estuaries.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Grant Hutchings, James Gattiker, Braden Scherting, Rodman R. Linn
Summary: Computational models for understanding and predicting fire in wildland and managed lands are becoming increasingly impactful. This paper addresses the characterization and population of mid-story fuels, which are not easily observable through traditional survey or remote sensing. The authors present a methodology to populate the mid-story using a generative model for fuel placement, which can be calibrated based on limited observation datasets or expert guidance. The connection of terrestrial LiDAR as the observations used to calibrate the generative model is emphasized. Code for the methods in this paper is provided.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Saswata Nandi, Pratiman Patel, Sabyasachi Swain
Summary: IMDLIB is an open-source Python library that simplifies the retrieval and processing of gridded meteorological data from IMD, enhancing data accessibility and facilitating hydro-climatic research and analysis.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Pengfei Wu, Jintao Liu, Meiyan Feng, Hu Liu
Summary: In this paper, a new flow distance algorithm called D infinity-TLI is proposed, which accurately estimates flow distance and width function using a two-segment-distance strategy and triangulation with linear interpolation method. The evaluation results show that D infinity-TLI outperforms existing algorithms and has a low mean absolute relative error.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
