Article
Engineering, Chemical
Fabian Westbrink, Alexander Elbel, Andreas Schwung, Steven X. Ding
Summary: Simulations with DEM require determining suitable material parameters to ensure validity and reliability of the results, a novel approach based on multi-objective reinforcement learning for material parameter optimization is proposed.
Article
Biochemical Research Methods
Ronald Colin Jaepel, Johannes Felix Buyel
Summary: The modeling of chromatographic separations can speed up downstream process development, but the calibration of parameters is a time-consuming challenge. Researchers designed a new approach based on Bayesian optimization and Gaussian processes, significantly reducing the computation time for chromatography parameters. Comparison studies on various datasets showed that Bayesian optimization consistently outperformed other methods in terms of computation speed and applicability for chromatography modeling.
JOURNAL OF CHROMATOGRAPHY A
(2022)
Article
Environmental Sciences
T. Yoshida, N. Hanasaki, K. Nishina, J. Boulange, M. Okada, P. A. Troch
Summary: This study presents a novel framework for calibrating global hydrological models (GHMs) by regionalizing parameters based on climate similarities. The results show that this method can achieve satisfactory and good simulation performances, and the identified parameters exhibit consistency with physical interpretations.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Lei Wu, Xia Liu, Junlai Chen, Xiaoyi Ma
Summary: This study introduces the non-dominated sorting genetic algorithm II (NSGA-II) to investigate the multi-objective synchronous calibration strategy, which utilizes multiple sources of information and exploits the data in better ways for obtaining high-cost performance results. The NSGA-II calibration can meet the accuracy requirements of runoff simulation and can better constrain the parameter process, making the calibrated model more in line with the physical conditions of the watershed, showing strong applicability.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Lei Wu, Xia Liu, Junlai Chen, Xiaoyi Ma
Summary: The NSGA-II algorithm has good adaptability in studying calibration performance, and can meet the requirements of different objectives and better constrain the parameter process, making the calibrated model more in line with the physical conditions of the watershed.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Francesco Fatone, Bartosz Szelag, Adam Kiczko, Dariusz Majerek, Monika Majewska, Jakub Drewnowski, Grzegorz Lagod
Summary: This article presents a methodology for constructing a simulator of catchment outflow hydrograph parameters using uncertainty analytical results obtained with the GLUE method. A novel sensitivity analysis of hydrodynamic catchment models was also developed for analyzing stormwater networks and underground infrastructure facilities. The impact of rainfall distribution and intensity on sensitivity factors, as well as the uncertainty of estimated coefficients in the simulator, was highlighted.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Environmental Sciences
Jong Seok Lee, Hyun Il Choi
Summary: Long-term streamflow simulations using Land Surface Models are crucial for understanding hydrological responses to climate change. Model calibration is necessary to improve simulation performance and stability. The study introduced an adjusted Kling and Gupta Efficiency (aKGE) to achieve a more balanced optimal solution, demonstrating improved simulation accuracy in high and average flow while maintaining a slightly lower correlation compared to traditional criteria.
Article
Geosciences, Multidisciplinary
Yanchen Zheng, Jianzhu Li, Ting Zhang, Youtong Rong, Ping Feng
Summary: Adopting new technologies for hydrological model calibration to produce accurate simulations for water resources or flood risk management is an important research topic. Incorporating hydrological signatures and concepts in model calibration has become prevalent. This research uses flood scaling property to constrain the multi-objective model calibration, taking into account the statistical relationship between flood peak, contributing areas, and catchment attributes. Recommended approach is the multi-objective calibration method, which improves model performance in terms of flow duration curve and reduces long-term runoff ratio bias.
Article
Environmental Sciences
Robert Chlumsky, Juliane Mai, James R. Craig, Bryan A. Tolson
Summary: The improvement of hydrological modeling frameworks allows for both model structure and parameters to be automatically calibrated and evaluated. The blended model structure calibration method can identify near-optimal model structures at significantly lower computational cost, as well as help identify dominant processes and model structures in catchments.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Siqi Wang, Ruisheng Diao, Chunlei Xu, Di Shi, Zhiwei Wang
Summary: The letter introduces a novel parameter calibration method based on off-policy deep reinforcement learning algorithm, which can automatically adjust incorrect parameter sets considering multiple events, saving a significant amount of work and improving model accuracy. The effectiveness of the proposed approach is verified through numerical experiments conducted on a realistic power plant model.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Construction & Building Technology
Hyunwoo Lim, Zhiqiang (John) Zhai
Summary: Interest in urban building energy modeling for evaluating effective urban energy management and carbon reduction policies is rising. Researchers proposed an urban building energy modeling method using a stochastic-deterministic-coupled approach to overcome limitations of using archetypes. The study verified the identifiability of unknown parameters in a building stock and evaluated the possibility of energy conservation measure analysis in the proposed model.
ENERGY AND BUILDINGS
(2022)
Article
Computer Science, Artificial Intelligence
Bowen Li, Qinyao Pan, Jie Zhong, Wenying Xu
Summary: This study is dedicated to investigating the long-run behavior estimation of temporal Boolean networks (TBNs) with multiple data losses, especially the asymptotical stability. The information transmission is modeled by Bernoulli variables, and an augmented system is constructed for analysis. A theorem shows that the asymptotical stability of the original system can be converted to that of the augmented system. A necessary and sufficient condition for asymptotical stability is obtained. Additionally, an auxiliary system is derived to study the synchronization issue of ideal TBNs and TBNs with multiple data losses, along with an effective criterion for verifying synchronization. Numerical examples are provided to demonstrate the validity of the theoretical results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Operations Research & Management Science
Phan Thien Thach
Summary: In this article, a symmetric duality for a multiple-objective problem is presented, constructed according to the quasi-conjugacy approach applied to nondecreasing homogeneous cost functions. The duality equation obtained helps to characterize the (weakly) efficient solutions of the primal problem and the dual.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2021)
Article
Chemistry, Physical
T. M. Beardsley, M. W. Matsen
Summary: The order-disorder transition of diblock copolymer melts was evaluated using field-theoretic simulations for an invariant polymerization index, with particular focus on the complex phase window. The results support the understanding that the gyroid phase extends down to the ODT, and predict that the Fddd phase can survive fluctuation effects, which is consistent with experimental findings.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Automation & Control Systems
Peng Dong, Junqing Li, Wei Guo, Junbin Lai, Feihong Mao, Kaifeng Wang, Xiangyang Xu, Shuhan Wang
Summary: This study proposes an integrated surrogate-assisted multi-objective optimization (ISAMO) framework to tackle the challenge of performing gearshifts with multiple clutches. The framework consists of a gearshift control strategy, a surrogate model based on radial basis function neural network (RBFNN) and an adaptive sampling method. The effectiveness of the proposed framework in improving optimization efficiency without sacrificing accuracy is validated.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Water Resources
Peter Csaki, Kitti Gyimothy, Peter Kalicz, Jan Szolgay, Katalin Anita Zagyvai-Kiss, Zoltan Gribovszki
JOURNAL OF HYDROLOGY AND HYDROMECHANICS
(2020)
Article
Water Resources
Jan Szolgay, Guenter Bloeschl, Zoltan Gribovszki, Juraj Parajka
JOURNAL OF HYDROLOGY AND HYDROMECHANICS
(2020)
Article
Water Resources
Ladislav Holko, Michal Danko, Patrik Sleziak
JOURNAL OF HYDROLOGY AND HYDROMECHANICS
(2020)
Article
Water Resources
Ladislav Holko, Patrik Sleziak, Michal Danko, Svetlana Bicarova, Joanna Pociask-Karteczka
JOURNAL OF HYDROLOGY AND HYDROMECHANICS
(2020)
Article
Water Resources
Babar Mujtaba, Hana Hlavacikova, Michal Danko, Joao L. M. P. de Lima, Ladislav Holko
JOURNAL OF HYDROLOGY AND HYDROMECHANICS
(2020)
Article
Environmental Sciences
Patrik Sleziak, Ladislav Holko, Michal Danko, Juraj Parajka
Article
Water Resources
Ladislav Holko, Michal Danko, Patrik Sleziak
Summary: The Jalovecky Creek catchment in Slovakia is likely the last big valley complex in the Carpathian Mountains where the hydrological cycle is still governed by natural processes. Recent analysis shows that the hydrological cycle has become more dynamic since 2014, but direct links with snow storage and snowmelt contribution to runoff were not confirmed. This suggests the need for further research to understand the changing dynamics of the hydrological cycle in the region.
HYDROLOGICAL PROCESSES
(2021)
Article
Environmental Sciences
Patrik Sleziak, Roman Vyleta, Kamila Hlavcova, Michaela Danacova, Milica Aleksic, Jan Szolgay, Silvia Kohnova
Summary: This study evaluated the possible climate change impacts on runoff regime in eight selected basins in Slovakia. The simulation indicated potential changes in the seasonality and extremality of long-term runoff, with increased winter runoff and decreased summer runoff expected in the future. The results provide valuable insights for policymakers and river basin authorities in planning and managing water resources under changing climate conditions.
Article
Environmental Sciences
Martin Kuban, Juraj Parajka, Rui Tong, Isabella Pfeil, Mariette Vreugdenhil, Patrik Sleziak, Brziak Adam, Jan Szolgay, Silvia Kohnova, Kamila Hlavcova
Summary: This paper examines the potential of a new dataset based on ASCAT satellite remote sensing of soil moisture for multiple objective calibrations of a hydrological model. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters and improving soil moisture and runoff simulation. The study suggests that this approach is particularly effective in areas with lower elevations and higher proportions of farming land use.
Article
Environmental Studies
Lenka Slavikova, Pavel Raska, Kazimierz Banasik, Marton Barta, Andras Kis, Silvia Kohnova, Piotr Matczak, Jan Szolgay
ENVIRONMENTAL HAZARDS-HUMAN AND POLICY DIMENSIONS
(2020)
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)