Article
Environmental Sciences
Nicholas M. Enwright, Wyatt C. Cheney, Kristine O. Evans, Hana R. Thurman, Mark S. Woodrey, Auriel M. V. Fournier, Dean B. Gesch, Jonathan L. Pitchford, Jason M. Stoker, Stephen C. Medeiros
Summary: This study developed a framework to create a probabilistic map of irregularly flooded wetlands using existing land use land cover data, lidar-derived digital elevation models, and Monte Carlo simulations. The results showed that this approach can provide valuable information about the spatial distribution and changes of coastal wetlands.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Geosciences, Multidisciplinary
Yan Liu, Jaime Fernandez-Ortega, Matias Mudarra, Andreas Hartmann
Summary: This study proposes an adapted version of Kling-Gupta efficiency (KGE) using a gamma distribution to solve problems with the original KGE in Markov chain Monte Carlo (MCMC) methods. The adapted KGE is used as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) algorithm. The results show that the adapted KGE is able to explore the posterior distributions of model parameters and reproduce system behaviors effectively, making it a feasible option for model calibration.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
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
Mathematics
Vasiliy V. Grigoriev, Petr N. Vabishchevich
Summary: Stochastic parameter estimation and inversion within the Bayesian framework, particularly using Markov chain Monte Carlo methods, are effective in solving complex inverse problems. This paper discusses a Bayesian approach for identifying adsorption and desorption rates in porous media reactive flow, with credible intervals plotted from sampled posterior distributions. The impact of noise in measurements and influence of multiple measurements for Bayesian identification procedure are studied, concluding on the effectiveness of MCMC sampling algorithm in determining acceptable parameters.
Article
Computer Science, Information Systems
Kushila Jayamanne, Zhe Ban, Madhusudhan Pandey, Ali Ghaderi, Bernt Lie
Summary: This study extends the previous findings on using No-U-Turn Sampler (NUTS) to estimate parameters of a generator model. It provides advice on configuring MCMC settings and discusses the impact of measurement data on posterior computation. The study also implements the classical MCMC technique, Metropolis, to estimate parameters and explores the fundamental process and terminology of MCMC. Finally, the knowledge gained is applied to select appropriate settings for NUTS to improve parameter estimation accuracy.
Article
Engineering, Civil
Pengfei Shi, Tao Yang, Bin Yong, Chong-Yu Xu, Zhenya Li, Xiaoyan Wang, Youwei Qin, Xudong Zhou
Summary: The Markov Chain Monte Carlo (MCMC) method is increasingly popular in uncertainty analysis of hydrological simulation. However, the estimation of the constant standard deviation (02) in the approach is subjective, hindering performance improvement. This study investigates the statistical meaning of parameter 02 and develops a new label called Confidence Level of Model (CLM) to interpret it. The MCMC method based on CLM performs well in generating regular posterior distributions and narrow, symmetrical confidence intervals.
JOURNAL OF HYDROLOGY
(2023)
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)
Article
Engineering, Multidisciplinary
Ke Zhang, Kailun Su, Yunhan Yao, Qingsong Li, Suan Chen
Summary: This paper presents a dynamic evaluation model of Markov chain Monte Carlo (MCMC) roundness error measurement uncertainty based on a stochastic process. The model samples the stochastic process using the MCMC method and calculates the state transition function to reflect the autocorrelation characteristics of the parameters. A comparison between high-precision and low-precision measurements verifies the accuracy and stability of the model, showing that the MCMC method is consistent with the traditional GUM method and Monte Carlo method. The MCMC method based on the stochastic process achieves dynamic evaluation of roundness error measurement uncertainty, obtaining accurate results and improving the evaluation accuracy.
Article
Physics, Fluids & Plasmas
Piotr Bialas, Piotr Korcyl, Tomasz Stebel
Summary: In this study, we investigate autocorrelations in neural Markov chain Monte Carlo (NMCMC) simulations, which is a variation of the traditional Metropolis algorithm using neural networks for independent proposals. Using the two-dimensional Ising model, we discuss different estimations of autocorrelation times in NMCMC and propose a loss function based on analytical results for the Metropolized independent sampler (MIS) to study its impact on autocorrelation times. Furthermore, we study the effects of implementing global discrete symmetries and partial heat-bath updates in the training process.
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
Mathematics
Erbet Almeida Costa, Carine de Menezes Rebello, Marcio Fontana, Leizer Schnitman, Idelfonso Bessa dos Reis Nogueira
Summary: Robust learning in Scientific Machine Learning (SciML) is addressed through a comprehensive methodology that considers various sources of uncertainties involved in model identification. The proposed approach provides an overall strategy for uncertainty-aware models in the SciML field.
Article
Statistics & Probability
Medha Agarwal, Dootika Vats
Summary: Autocovariances are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations. This paper proposes a globally centered estimator of the autocovariance function (G-ACvF) for multiple-chain MCMC sampling, which exhibits significant improvements over current methods. The impact of this improved estimator is evident in various critical output analysis applications.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(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
Biochemical Research Methods
Ziting Yuan, Yota Yamamoto, Tomoyuki Yajima, Yoshiaki Kawajiri
Summary: Model-based design and optimization methods are used to facilitate industrial applications of chromatographic separations. The uncertainty of model parameters needs to be quantified for robust design and control. This study proposes an approach based on the sequential Monte Carlo (SMC) method and the Bayesian principle to estimate parameter uncertainty. An example of the linear driving force model for phenol and p-cresol separation is used to validate the necessity of pulse injection and breakthrough experiments for accurate and precise parameter estimation. Careful modeling of observation errors is also found to be critical for obtaining reasonable estimation.
JOURNAL OF CHROMATOGRAPHY A
(2023)
Article
Computer Science, Interdisciplinary Applications
DanHua ShangGuan
Summary: The Monte Carlo method is a powerful tool in many research fields, but the increasing complexity of physical models and mathematical models requires efficient algorithms to overcome the computational cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Geochemistry & Geophysics
Michele Meroni, Dominique Fasbender, Riad Balaghi, Mustapha Dali, Myriam Haffani, Ismael Haythem, Josh Hooker, Mouanis Lahlou, Raul Lopez-Lozano, Hamid Mahyou, Moncef Ben Moussa, Nabil Sghaier, Talhaoui Wafa, Olivier Leo
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2016)
Article
Remote Sensing
Michele Meroni, Felix Rembold, Dominique Fasbender, Anton Vrieling
REMOTE SENSING LETTERS
(2017)
Article
Remote Sensing
Michele Meroni, Anne Schucknecht, Dominique Fasbender, Felix Rembold, Francesco Fava, Margaux Mauclaire, Deborah Goffner, Luisa M. Di Lucchio, Ugo Leonardi
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2017)
Article
Environmental Sciences
Michele Meroni, Dominique Fasbender, Felix Rembold, Clement Atzberger, Anja Klisch
REMOTE SENSING OF ENVIRONMENT
(2019)
Article
Environmental Sciences
Michele Meroni, Dominique Fasbender, Raul Lopez-Lozano, Mirco Migliavacca
Article
Environmental Sciences
Dominique Fasbender, Blanka Vajsova, Csaba Wirnhardt, Slavko Lemajic
Article
Medicine, General & Internal
Rocio Martin-Canavate, Estefania Custodio, Abukar Yusuf, Daniel Molla, Dominique Fasbender, Francois Kayitakire
Review
Agronomy
Yacouba Ouedraogo, Jean-Baptiste Sibiri Taonda, Idriss Serme, Bernard Tychon, Charles L. Bielders
Article
Environmental Sciences
Blanka Vajsova, Dominique Fasbender, Csaba Wirnhardt, Slavko Lemajic, Wim Devos
Article
Soil Science
Martin Zanutel, Sarah Garre, Charles L. Bielders
Summary: This study assessed the long-term effect of century-old charcoal on different textured soils, with limited impact found on soil physical properties, mainly in terms of pore size distribution and water retention capability.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2022)
Article
Environmental Sciences
Amaury Frankl, Maarten De Boever, Jonas Bodyn, Saskia Buysens, Liesbet Rosseel, Sarah Deprez, Charles Bielders, Aurore Degre, Alexia Stokes
Summary: Vegetative barriers are used to reduce sediment export from cropland and mitigate negative off-site consequences of soil erosion. Among the three types of barriers studied, those made of coconut-fibre bales showed superior performance in regulating runoff and sediment, but the accumulation of sediment inside the structures may increase the risk of bypassing or overtopping the barriers.
LAND DEGRADATION & DEVELOPMENT
(2021)
Article
Environmental Sciences
Zonirina Ramahaimandimby, Alain Randriamaherisoa, Marnik Vanclooster, Charles L. Bielders
Summary: Understanding the hydrological behavior of watersheds and their driving factors is crucial for sustainable water resources management. In this study, the key factors influencing the hydrological signatures of four watersheds in northeastern Madagascar were identified. Land cover, soil properties, and geology were found to be the best predictors for certain hydrological characteristics. These findings provide valuable insights into the key drivers of hydrological behavior that can inform water resource management strategies.
Article
Multidisciplinary Sciences
Alice Alonso, Manuel Froidevaux, Mathieu Javaux, Eric Laloy, Samuel Mattern, Christian Roisin, Marnik Vanclooster, Charles Bielders
Summary: This article provides high-resolution datasets of penetrometer resistance and soil bulk density for studying the impact of different tillage treatments on soil structure and physical quality. The data can be used to understand soil hydraulic and physical quality and develop soil transfer functions.
Proceedings Paper
Remote Sensing
Michele Meroni, Anne Schucknecht, Dominique Fasbender, Felix Rembold, Francesco Fava, Margaux Mauclaire, Deborah Goffner, Luisa M. Di Lucchio, Ugo Leonardi
2017 9TH INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES (MULTITEMP)
(2017)
Article
Agricultural Economics & Policy
Marco Bertaglia, Vincenzo Angileri, Dominique Fasbender
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)