Article
Green & Sustainable Science & Technology
Fengchang Jiang, John Awaitey, Haiyan Xie
Summary: This research analyzed construction costs by adopting multivariate cost prediction models to predict construction cost index (CCI) and other independent variables. The study found that the ARIMA model is the most accurate forecasting model, and the number of building permits issued, the consumer price index, the amount of money supply in the country, the producer price index, and the import price index are influencing factors for short to medium-term investment decisions.
Review
Biology
Alex Eric Yuan, Wenying Shou
Summary: This article provides a critical review of three statistical causal discovery methods and their applications in ecological processes. The review examines what each method tests for, the causal statements it implies, and the potential for misinterpretation. The authors introduce new visualization techniques and highlight the limitations of so-called "model-free" causality tests. The goal of the review is to encourage thoughtful application of these methods, facilitate interdisciplinary communication, and promote explicit assumptions.
Article
Geosciences, Multidisciplinary
Davide Sartirana, Marco Rotiroti, Tullia Bonomi, Mattia De Amicis, Veronica Nava, Letizia Fumagalli, Chiara Zanotti
Summary: The increase in urbanization has led to greater interaction between groundwater and underground infrastructure. Analyzing groundwater time-series using a data-driven approach can improve urban conceptual models and help design underground development.
HYDROGEOLOGY JOURNAL
(2022)
Article
Environmental Sciences
Jun Sun, Feng Ye, Nadia Nedjah, Ming Zhang, Dong Xu
Summary: In order to meet the demand for real-time and accurate hydrologic data analysis, a real-time statistical analysis library called HydroStreamingLib based on the new generation of big data processing engine Flink was proposed and implemented. A real-time statistical analysis system of hydrologic stream data was developed based on this library, which proved its efficiency and handiness.
Article
Sport Sciences
David Meechan, Stuart A. McErlain-Naylor, John J. McMahon, Timothy J. Suchomel, Paul Comfort
Summary: This study compared the effect of load on force and velocity curves during the countermovement shrug (CMS) in weightlifting derivatives. The results showed changes in negative velocity and positive velocity at different stages. A relative load of 40% 1RM PC maximized propulsion velocity, while 140% 1RM maximized force.
JOURNAL OF SPORTS SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Alperen Karan, Atabey Kaygun
Summary: This paper introduces topological data analysis methods for classification tasks on univariate time series, enhancing accuracy and reducing noise through stable topological features and subwindow processing.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Physics, Fluids & Plasmas
Sangwon Lee, Vipul Periwal, Junghyo Jo
Summary: Inferring dynamics from incomplete time series data is challenging, but an expectation maximization algorithm proposed in this study demonstrates effectiveness in restoring missing data points and inferring underlying network models. Balancing consistency between observed and missing data points is crucial for accurate model inference during iterative processes.
Article
Engineering, Mechanical
Huimei Ma, Xiaofan Lu, Linan Zhang
Summary: In this paper, a data-driven regression approach is proposed to identify parametric governing equations from time-series data. Iterative computations are performed for each time stamp to determine if the governing equations to be recovered are time dependent. The results are then used to extract the parametric equations. The proposed method outperforms other sparse-promoting algorithms in identifying parametric differential equations in the low-noise regime in terms of accuracy and computation time.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Chemical
Alla Abdella, Jeffrey K. Brecht, Ismail Uysal
Summary: This paper conducts a statistical and temporal analysis on a novel location aware multivariate time series dataset to analyze the temporal heterogeneity, complexity, similarity, and discrepancy. The dataset includes temperature variations across different shipments with 9 sensors monitoring data, providing descriptive statistics and time series visualization for other researchers. Potential use cases of machine learning and data analytics in various application domains are also discussed.
JOURNAL OF FOOD ENGINEERING
(2021)
Article
Physics, Multidisciplinary
Anass B. El-Yaagoubi, Moo K. Chung, Hernando Ombao
Summary: This article introduces the concepts and applications of topological data analysis (TDA), as well as a method for analyzing multivariate time series data. The focus is on the application to multivariate brain signals and brain connectivity networks, and the article explores some open problems and potential applications in modeling brain network directionality and capturing variations in topological properties of data collected from multiple subjects.
Article
Mathematics, Interdisciplinary Applications
Antonio Samuel Alves Silva, Romulo Simoes Cezar Menezes, Osvaldo A. Rosso, Borko Stosic, Tatijana Stosic
Summary: This study analyzed the predictability and complexity of monthly rainfall temporal series in Pernambuco state, northeastern Brazil using complexity entropy causality plane and Fisher Shannon plane. By comparing the positions in these planes, different rainfall regimes in inland, intermediate, and coastal regions were distinguished. Time dependent analysis identified periods of higher entropy related to El Nino episodes and historical droughts in the Sertao region.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Serkan Balli
Summary: The Covid-19 pandemic is the most important health disaster the world has faced in the past eight months, predicting its trend has become a challenge. A study analyzed COVID-19 data and proposed a time series prediction model, estimating the global pandemic will peak at the end of January 2021 with approximately 80 million people cumulatively infected.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Environmental Sciences
Bingshi Liu, Xiancai Zou, Shuang Yi, Nico Sneeuw, Jiancheng Li, Jianqiang Cai
Summary: This study proposes a statistical model driven by precipitation and temperature data to reconstruct mass anomalies of high mountain glaciers. The method shows good performance in predicting and reconstructing mass anomalies, and provides valuable information for the sustainable management and protection of water resources.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Mathematics
Gholamreza Hesamian, Faezeh Torkian, Arne Johannssen, Nataliya Chukhrova
Summary: An exponential autoregressive model for complex time series data is presented in this paper, and a three-step procedure based on quantile methods is proposed for estimating the parameters of this nonlinear model. The performance of the introduced model is evaluated using four established goodness-of-fit criteria, and its practical utility is showcased through simulation studies and real-world data illustrations.
Article
Computer Science, Theory & Methods
Muhammad Aslam
Summary: In this paper, a semi-average method based on neutrosophic statistics is introduced to measure the trend in imprecise or interval data. This method can be applied to imprecise or interval data, which cannot be achieved by the traditional semi-average method in classical statistics. The application of the proposed method is demonstrated using wind speed data, and its efficiency is compared with the classical semi-average method in terms of information and adequacy.
JOURNAL OF BIG DATA
(2023)
Article
Engineering, Civil
Giada Felisa, Giulio Panini, Pietro Pedrazzoli, Vittorio Di Federico
Summary: Water stress conditions associated with population growth, climate change, and groundwater contamination require increasing the resilience and sustainability of water supply systems. This can be achieved through a comprehensive approach that combines groundwater models and water management models to assess water availability and develop effective management strategies.
WATER RESOURCES MANAGEMENT
(2022)
Article
Mechanics
Farhad Zeighami, Alessandro Lenci, Vittorio Di Federico
Summary: We developed a sharp-interface model to study the propagation of buoyancy-driven flow in fractured and porous media. The model takes into account the effects of spatial heterogeneity and fluid rheology. The flow behavior is described using self-similar solutions and nonlinear ordinary differential equations. The results show that the flow shape is influenced by the rheological index and the spatial variability of the aperture, and the residual liquid mass exhibits a negative power-law behavior with time, dependent on the rheological index and the aperture variation.
JOURNAL OF NON-NEWTONIAN FLUID MECHANICS
(2022)
Article
Engineering, Civil
Farhad Zeighami, Leonardo Sandoval, Alberto Guadagnini, Vittorio Di Federico
Summary: Seismic metabarriers are designed to reduce ground-induced vibrations and protect vulnerable structures from seismic surface waves by using an array of locally resonant elements. This study investigates the influence of three key mechanical parameters (soil density, soil shear modulus, and resonator mass) on the seismic isolation performance of the metabarrier. Global sensitivity analysis techniques are employed to quantify the uncertainties associated with these parameters. The results show that the shear modulus has the most significant influence on the transmission coefficient of the metabarrier across the entire frequency range of interest.
ENGINEERING STRUCTURES
(2023)
Article
Physics, Applied
Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky
Summary: This article presents an approach for modelling hysteresis in piezoelectric materials using sparse regression techniques. The study demonstrates the efficiency and accuracy of the proposed approach through numerical experiments and comparisons with traditional regression-based and neural network methods. The source code is available for further exploration and implementation.
APPLIED PHYSICS LETTERS
(2023)
Article
Electrochemistry
Weiyu Li, Hamdi A. Tchelepi, Daniel M. Tartakovsky
Summary: The study analyzed the role of buffer layer materials in lithium batteries and identified the conditions under which the buffer layer stabilizes electrodeposition and suppresses dendrite growth. The model predicted the effectiveness of several prospective buffer materials in stabilizing electrodeposition and suppressing dendrite growth, which was consistent with experimental findings. This has important implications for guiding the experimental and computational discovery of new buffer materials.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2023)
Article
Environmental Sciences
Weiyu Li, Daniel M. Tartakovsky
Summary: Accurately estimating evapotranspiration is crucial for smart agriculture and sustainable groundwater management. We present novel methods, EnKF and MLE, that can infer spatially varying ET rates and root water uptake profiles from soil-moisture measurements. Our methods accurately estimate total ET rates and root-uptake profiles in a drip irrigation setting and are up to two orders of magnitude faster than the standard EnKF.
WATER RESOURCES RESEARCH
(2023)
Article
Mathematics, Applied
Hannah Lu, Francesco Giannino, Daniel M. Tartakovsky
Summary: Mathematical models play a key role in estimating patient-specific initial viral load, predicting the course of infection, etc. The development of COVID-19 pandemics has led to increasingly complex models. We found that the widely used Target Cell Limited model fails the identifiability test, but we propose an identifiable and parsimonious model that matches observations and predictions of more complex counterparts.
APPLIED MATHEMATICS LETTERS
(2023)
Article
Computer Science, Interdisciplinary Applications
Hannah Lu, Daniel M. Tartakovsky
Summary: DRIPS is a new model reduction framework that combines offline local model reduction with online parameter interpolation. By using dynamic mode decomposition to build a low-rank linear surrogate model, it is able to directly model quantities of interest and has higher computational efficiency compared to traditional proper orthogonal decomposition methods.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Electrochemistry
Weiyu Li, Daniel M. Tartakovsky
Summary: This study presents a homogenized thermal model for a spherical active particle coated with a carbon binder domain (CBD) immersed in a liquid electrolyte. The model replaces the composite particle with a homogeneous particle with equivalent thermal conductivity, preserving the amount of released heat at the solid/electrolyte interface. This analytical expression for thermal conductivity can be readily integrated into thermal simulations, providing a means to account for CBD in battery design and management models.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2023)
Article
Mechanics
N. Merli, S. Longo, L. Chiapponi, V. Di Federico
Summary: This study investigates the impact of fluid rheology on flow in a finite rock fracture with varying aperture and competing drainage mechanisms. The flow is caused by the release of fluid, with rheology options including Newtonian, Ostwald-deWaele, or Herschel-Bulkley fluids. The Hele-Shaw analogy allows extending these findings to porous media. The results show that various factors, such as fluid rheology, fracture geometry, and ambient depth, affect the profile of the current and volume remaining in the fracture, with drainage times varying significantly. The theoretical model is validated through experiments, demonstrating good agreement between theory and experimental results.
Article
Energy & Fuels
Livia Paiva Fulchignoni, Daniel M. Tartakovsky
Summary: Equations of state (EoS) are crucial for modeling fluid mixtures' phase equilibrium, but their parameterization involves fitting models to experimental data via nonlinear, non-convex, multivariate optimization. We show that subjective choices of optimization algorithms and initial guesses impact EoS predictions, resulting in fundamental uncertainties even after tuning to limited experimental data. Using two hydrocarbon reservoir fluids as examples, we demonstrate dramatic differences in predicting the fluids' thermophysical behavior in unsampled pressure and temperature regions depending on EoS parameterizations. We propose a probabilistic treatment of design variables to quantify the predictive uncertainty of resulting fluid models.
GEOENERGY SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
Livia Paiva Fulchignoni, Christiano Garcia da Silva Santim, Daniel M. Tartakovsky
Summary: Offshore development requires significant investments that are subject to multiple uncertainties. Quantifying the uncertainties in reservoir production predictions and project revenue can mitigate risks and enable more informed business decisions.
GEOENERGY SCIENCE AND ENGINEERING
(2023)
Article
Physics, Fluids & Plasmas
Gerardo Severino, Francesco Giannino, Francesco De Paola, Vittorio Di Federico
Summary: We investigate 2D incompressible inertial flows through porous media and find that the constitutive, nonlinear model can be transformed into a linear one by introducing a new parameter K* encompassing all inertial effects at small scales. For naturally occurring formations at large scales, K* varies erratically, and we analytically compute its counterpart, termed generalized effective conductivity, using the self-consistent approach (SCA). Despite its approximate nature, SCA yields simple results that agree well with Monte Carlo simulations.
Article
Computer Science, Interdisciplinary Applications
Apoorv Srivastava, Wei Kang, Daniel M. Tartakovsky
Summary: This paper introduces a mathematical formulation of feature-informed data assimilation (FIDA), which utilizes the information about feature events in dynamical systems to estimate state variables and unknown parameters. The observation operator in FIDA is a set-valued functional, which is different from conventional data assimilation. Through three numerical experiments, FIDA's ability to estimate model parameters from noisy observations is demonstrated.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Mathematics, Applied
Wenjie Shi, Daniel M. Tartakovsky
Summary: The study investigates the impact of stiffness of random ODEs on gPC performance and introduces gPC with parallel MIRK schemes to solve random stiff ODEs. The stiffness analysis and computational experiments validate the feasibility and effectiveness of the method in solving random ODEs.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2022)
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)