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Journal articles on the topic 'Initialization uncertainty'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Celemin, Carlos E., and Miguel A. Melgarejo. "A Proposal to Speed up the Computation of the Centroid of an Interval Type-2 Fuzzy Set." Advances in Fuzzy Systems 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/158969.

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This paper presents two new algorithms that speed up the centroid computation of an interval type-2 fuzzy set. The algorithms include precomputation of the main operations and initialization based on the concept of uncertainty bounds. Simulations over different kinds of footprints of uncertainty reveal that the new algorithms achieve computation time reductions with respect to the Enhanced-Karnik algorithm, ranging from 40 to 70%. The results suggest that the initialization used in the new algorithms effectively reduces the number of iterations to compute the extreme points of the interval cen

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Akil, Nicolas, Guillaume Artigue, Michaël Savary, Anne Johannet, and Marc Vinches. "Uncertainty Estimation in Hydrogeological Forecasting with Neural Networks: Impact of Spatial Distribution of Rainfalls and Random Initialization of the Model." Water 13, no. 12 (2021): 1690. http://dx.doi.org/10.3390/w13121690.

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Neural networks are used to forecast hydrogeological risks, such as droughts and floods. However, uncertainties generated by these models are difficult to assess, possibly leading to a low use of these solutions by water managers. These uncertainties are the result of three sources: input data, model architecture and parameters and their initialization. The aim of the study is, first, to calibrate a model to predict Champagne chalk groundwater level at Vailly (Grand-Est, France), and, second, to estimate related uncertainties, linked both to the spatial distribution of rainfalls and to the par

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Payne, Mark R., Manuel Barange, William W. L. Cheung, et al. "Uncertainties in projecting climate-change impacts in marine ecosystems." ICES Journal of Marine Science 73, no. 5 (2015): 1272–82. http://dx.doi.org/10.1093/icesjms/fsv231.

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Abstract Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal var

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Hidasi, Balazs, and Domonkos Tikk. "Initializing Matrix Factorization Methods on Implicit Feedback Databases." JUCS - Journal of Universal Computer Science 19, no. (12) (2013): 1834–53. https://doi.org/10.3217/jucs-019-12-1834.

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The implicit feedback based recommendation problem--when only the user history is available but there are no ratings--is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Recently, implicit feedback problem is being received more attention, as application oriented research gets more attractive within the field. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the

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Koziol, Conrad P., Joe A. Todd, Daniel N. Goldberg, and James R. Maddison. "fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models." Geoscientific Model Development 14, no. 9 (2021): 5843–61. http://dx.doi.org/10.5194/gmd-14-5843-2021.

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Abstract. Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions of

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Koster, Randal D., Anthony M. DeAngelis, Siegfried D. Schubert, and Andrea M. Molod. "Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization." Journal of Hydrometeorology 22, no. 10 (2021): 2505–19. http://dx.doi.org/10.1175/jhm-d-21-0022.1.

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AbstractSoil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moistur

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Hong, Euntae, and Jongwoo Lim. "Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation." Sensors 18, no. 12 (2018): 4287. http://dx.doi.org/10.3390/s18124287.

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Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs wi

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Albrecht, Jan Christoph, Anna Lucia Romero Müller, and Florian Rüttger. "Dealing with uncertainty regarding the initialization and budgeting of digital transformation projects." Procedia Computer Science 256 (2025): 1575–82. https://doi.org/10.1016/j.procs.2025.02.293.

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Xu, Qiuhui, Shenfang Yuan, and Tianxiang Huang. "Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty." Sensors 21, no. 4 (2021): 1283. http://dx.doi.org/10.3390/s21041283.

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Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected by various uncertainties. In addition, crack-sensitive GW features are influenced by time-varying conditions which further increase the difficulty in crack quantification. Considering these uncertainties, the Gaussian mixture model (GMM) is studied to model the probability distribution of GW features. To further improve the accur

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Mozyrsky, Dima, and Vladimir Privman. "Quantum Signal Splitting that Avoids Initialization of the Targets." Modern Physics Letters B 11, no. 29 (1997): 1277–83. http://dx.doi.org/10.1142/s0217984997001523.

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The classical signal splitting and copying are not possible in quantum mechanics. Specifically, one cannot copy the basis up and down states of the input (I) two-state system (qubit, spin) into the copy (C) and duplicate-copy (D) two-state systems if the latter systems are initially in an arbitrary state. We consider instead a quantum evolution in which the basis states of I at time t are duplicated in at least two of the systems I, C, D, at time t+Δt. In essence, the restriction on the initial target states is exchanged for uncertainty as to which two of the three qubits retain copies of the

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Alavi, Nasim, Gordon Drewitt, Aaron A. Berg, and William J. Merryfield. "Soil Moisture Initialization Effects in the CCCma AGCM3: Relationship of Sub-Seasonal Climate Forecast Error to Uncertainty in Soil Moisture Initializations." Atmosphere-Ocean 49, no. 3 (2011): 179–88. http://dx.doi.org/10.1080/07055900.2011.608422.

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Koster, Randal D., Siegfried D. Schubert, Anthony M. DeAngelis, Andrea M. Molod, and Sarith P. Mahanama. "Using a Simple Water Balance Framework to Quantify the Impact of Soil Moisture Initialization on Subseasonal Evapotranspiration and Air Temperature Forecasts." Journal of Hydrometeorology 21, no. 8 (2020): 1705–22. http://dx.doi.org/10.1175/jhm-d-20-0007.1.

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AbstractPast studies have shown that accurate soil moisture initialization can contribute significant skill to near-surface air temperature (T2M) forecasts at subseasonal leads. The mechanisms by which soil moisture contributes such skill are examined here with a simple water balance–based model that captures the essence of soil moisture behavior in a state-of-the-art subseasonal-to-seasonal (S2S) forecasting system. The simple model successfully transforms initial soil moisture contents into average “forecast” evapotranspiration (ET) values at 16–30-day lead that agree well, during summer, wi

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Sapriza-Azuri, Gonzalo, Pablo Gamazo, Saman Razavi, and Howard S. Wheater. "On the appropriate definition of soil profile configuration and initial conditions for land surface–hydrology models in cold regions." Hydrology and Earth System Sciences 22, no. 6 (2018): 3295–309. http://dx.doi.org/10.5194/hess-22-3295-2018.

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Abstract. Arctic and subarctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, carbon cycle, and hydrology in Earth system models. This study focuses on land surface models (LSMs) that represent the lower boundary condition of general circulation models (GCMs) and regional climate models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically

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Descamps, L., and O. Talagrand. "On Some Aspects of the Definition of Initial Conditions for Ensemble Prediction." Monthly Weather Review 135, no. 9 (2007): 3260–72. http://dx.doi.org/10.1175/mwr3452.1.

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Abstract Four methods for initialization of ensemble forecasts are systematically compared, namely the methods of singular vectors (SV) and bred modes (BM), as well as the ensemble Kalman filter (EnKF) and the ensemble transform Kalman filter (ETKF). The comparison is done on synthetic data with two models of the flow, namely, a low-order model introduced by Lorenz and a three-level quasigeostrophic atmospheric model. For the latter, both cases of a perfect and an imperfect model are considered. The performance of the various initialization methods is assessed in terms of the statistical relia

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Burgard, Clara, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe. "The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation." Cryosphere 14, no. 7 (2020): 2387–407. http://dx.doi.org/10.5194/tc-14-2387-2020.

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Abstract. The observational uncertainty in sea ice concentration estimates from remotely sensed passive microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarization from standard output of an Earth System Model. To evaluate sources of uncertainties when applying ARC3O, we compare brightness temperatures simulated by applying ARC3O on three assimilation runs of the

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Yu, Danyang, Jinzhong Yang, Liangsheng Shi, et al. "On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling." Hydrology and Earth System Sciences 23, no. 7 (2019): 2897–914. http://dx.doi.org/10.5194/hess-23-2897-2019.

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Abstract. Soil water movement has direct effects on environment, agriculture and hydrology. Simulation of soil water movement requires accurate determination of model parameters as well as initial and boundary conditions. However, it is difficult to obtain the accurate initial soil moisture or matric potential profile at the beginning of simulation time, making it necessary to run the simulation model from the arbitrary initial condition until the uncertainty of the initial condition (UIC) diminishes, which is often known as “warming up”. In this paper, we compare two commonly used methods for

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Mondelli, Marco, and Ramji Venkataramanan. "Approximate message passing with spectral initialization for generalized linear models*." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 11 (2022): 114003. http://dx.doi.org/10.1088/1742-5468/ac9828.

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Abstract We consider the problem of estimating a signal from measurements obtained via a generalized linear model. We focus on estimators based on approximate message passing (AMP), a family of iterative algorithms with many appealing features: the performance of AMP in the high-dimensional limit can be succinctly characterized under suitable model assumptions; AMP can also be tailored to the empirical distribution of the signal entries, and for a wide class of estimation problems, AMP is conjectured to be optimal among all polynomial-time algorithms. However, a major issue of AMP is that in m

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Illing, Sebastian, Christopher Kadow, Holger Pohlmann, and Claudia Timmreck. "Assessing the impact of a future volcanic eruption on decadal predictions." Earth System Dynamics 9, no. 2 (2018): 701–15. http://dx.doi.org/10.5194/esd-9-701-2018.

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Abstract. The likelihood of a large volcanic eruption in the future provides the largest uncertainty concerning the evolution of the climate system on the timescale of a few years, but also an excellent opportunity to learn about the behavior of the climate system, and our models thereof. So the following question emerges: how predictable is the response of the climate system to future eruptions? By this we mean to what extent will the volcanic perturbation affect decadal climate predictions and how does the pre-eruption climate state influence the impact of the volcanic signal on the predicti

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Jager, Eliot, Fabien Gillet-Chaulet, Nicolas Champollion, Romain Millan, Heiko Goelzer, and Jérémie Mouginot. "The future of Upernavik Isstrøm through the ISMIP6 framework: sensitivity analysis and Bayesian calibration of ensemble prediction." Cryosphere 18, no. 11 (2024): 5519–50. http://dx.doi.org/10.5194/tc-18-5519-2024.

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Abstract. This study investigates the uncertain future contribution to sea-level rise in response to global warming of Upernavik Isstrøm, a tidewater glacier in Greenland. We analyse multiple sources of uncertainty, including Shared Socioeconomic Pathways (SSPs), climate models (global and regional), ice–ocean interactions, and ice sheet model (ISM) parameters. We use weighting methods based on spatio-temporal velocity and elevation data to reduce ice flow model uncertainty and evaluate their ability to prevent overconfidence. Our developed initialization method demonstrates the capability of

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Hamill, Thomas M., and Michael Scheuerer. "Improving Ensemble Weather Prediction System Initialization: Disentangling the Contributions from Model Systematic Errors and Initial Perturbation Size." Monthly Weather Review 149, no. 1 (2021): 77–90. http://dx.doi.org/10.1175/mwr-d-20-0119.1.

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AbstractCharacteristics of the European Centre for Medium-Range Weather Forecast’s (ECMWF’s) 0000 UTC diagnosed 2-m temperatures (T2m) from 4D-Var and global ensemble forecasts initial conditions were examined in 2018 over the contiguous United States at 1/2° grid spacing. These were compared against independently generated, upscaled high-resolution T2m analyses that were created with a somewhat novel data assimilation methodology, an extension of classical optimal interpolation (OI) to surface data analysis. The analysis used a high-resolution, spatially detailed climatological background and

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Laub, Moritz, Michael Scott Demyan, Yvonne Funkuin Nkwain, et al. "DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models." Biogeosciences 17, no. 6 (2020): 1393–413. http://dx.doi.org/10.5194/bg-17-1393-2020.

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Abstract. Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the a

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Alevropoulos-Borrill, Alanna V., Isabel J. Nias, Antony J. Payne, Nicholas R. Golledge, and Rory J. Bingham. "Ocean-forced evolution of the Amundsen Sea catchment, West Antarctica, by 2100." Cryosphere 14, no. 4 (2020): 1245–58. http://dx.doi.org/10.5194/tc-14-1245-2020.

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Abstract. The response of ice streams in the Amundsen Sea Embayment (ASE) to future climate forcing is highly uncertain. Here we present projections of 21st century response of ASE ice streams to modelled local ocean temperature change using a subset of Coupled Model Intercomparison Project (CMIP5) simulations. We use the BISICLES adaptive mesh refinement (AMR) ice sheet model, with high-resolution grounding line resolving capabilities, to explore grounding line migration in response to projected sub-ice-shelf basal melting. We find a contribution to sea level rise of between 2.0 and 4.5 cm by

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Kanari, Eva, Lauric Cécillon, François Baudin, et al. "A robust initialization method for accurate soil organic carbon simulations." Biogeosciences 19, no. 2 (2022): 375–87. http://dx.doi.org/10.5194/bg-19-375-2022.

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Abstract. Changes in soil organic carbon (SOC) stocks are a major source of uncertainty for the evolution of atmospheric CO2 concentration during the 21st century. They are usually simulated by models dividing SOC into conceptual pools with contrasted turnover times. The lack of reliable methods to initialize these models, by correctly distributing soil carbon amongst their kinetic pools, strongly limits the accuracy of their simulations. Here, we demonstrate that PARTYSOC, a machine-learning model based on Rock-Eval® thermal analysis, optimally partitions the active- and stable-SOC pools of A

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M., Nithya, K. Balasubramaium, and Senthil S. "MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications." Journal of Electrical and Electronic Engineering 12, no. 2 (2024): 36–47. http://dx.doi.org/10.11648/j.jeee.20241202.12.

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Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuiti

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Neff, P., D. Steineder, B. Stummer, and T. Clemens. "Estimation of Initial Hydrocarbon Saturation Applying Machine Learning Under Petrophysical Uncertainty." SPE Reservoir Evaluation & Engineering 24, no. 02 (2021): 325–40. http://dx.doi.org/10.2118/203384-pa.

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Summary The initial hydrocarbon saturation has a major effect on field-development planning and resource estimation. However, the bases of the initial hydrocarbon saturation are indirect measurements from spatially distributed wells applying saturation-height modeling using uncertain parameters. Because of the multitude of parameters, applying assisted-matching methods requires trade-offs regarding the quality of objective functions used for the various observed data. Applying machine learning (ML) in a Bayesian framework helps overcome these challenges. In the present study, the methodology i

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Ajami, H., J. P. Evans, M. F. McCabe, and S. Stisen. "Technical Note: Reducing the spin-up time of integrated surface water–groundwater models." Hydrology and Earth System Sciences Discussions 11, no. 6 (2014): 6969–92. http://dx.doi.org/10.5194/hessd-11-6969-2014.

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Abstract. One of the main challenges in catchment scale application of coupled/integrated hydrologic models is specifying a catchment's initial conditions in terms of soil moisture and depth to water table (DTWT) distributions. One approach to reduce uncertainty in model initialization is to run the model recursively using a single or multiple years of forcing data until the system equilibrates with respect to state and diagnostic variables. However, such "spin-up" approaches often require many years of simulations, making them computationally intensive. In this study, a new hybrid approach wa

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Vieira, Guilherme S., Irina I. Rypina, and Michael R. Allshouse. "Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts." Fluids 5, no. 4 (2020): 184. http://dx.doi.org/10.3390/fluids5040184.

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Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced sp

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Carrassi, A., R. J. T. Weber, V. Guemas, F. J. Doblas-Reyes, M. Asif, and D. Volpi. "Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations." Nonlinear Processes in Geophysics 21, no. 2 (2014): 521–37. http://dx.doi.org/10.5194/npg-21-521-2014.

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Abstract. Initialization techniques for seasonal-to-decadal climate predictions fall into two main categories; namely full-field initialization (FFI) and anomaly initialization (AI). In the FFI case the initial model state is replaced by the best possible available estimate of the real state. By doing so the initial error is efficiently reduced but, due to the unavoidable presence of model deficiencies, once the model is let free to run a prediction, its trajectory drifts away from the observations no matter how small the initial error is. This problem is partly overcome with AI where the aim

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Veneruso, Paolo, Roberto Opromolla, Carlo Tiana, Giacomo Gentile, and Giancarmine Fasano. "Sensing Requirements and Vision-Aided Navigation Algorithms for Vertical Landing in Good and Low Visibility UAM Scenarios." Remote Sensing 14, no. 15 (2022): 3764. http://dx.doi.org/10.3390/rs14153764.

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To support the rapid development of the Urban Air Mobility framework, safe navigation must be ensured to Vertical Take-Off and Landing aircraft, especially in the approach and landing phases. Visual sensors have the potential of providing accurate measurements with reduced budgets, although integrity issues, as well as performance degradation in low visibility and highly dynamic environments, may pose challenges. In this context, this paper focuses on autonomous navigation during vertical approach and landing procedures and provides three main contributions. First, visual sensing requirements

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Xu, Haoding, Xuzhen He, and Daichao Sheng. "Rainfall-Induced Landslides from Initialization to Post-Failure Flows: Stochastic Analysis with Machine Learning." Mathematics 10, no. 23 (2022): 4426. http://dx.doi.org/10.3390/math10234426.

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Rainfall-induced landslides represent a severe hazard around the world due to their sudden occurrence, as well as their widespread influence and runout distance. Considering the spatial variability of soil, stochastic analysis is often conducted to give a probability description of the runout. However, rainfall-induced landslides are complex and time-consuming for brute-force Monte Carlo analyses. Therefore, new methods are required to improve the efficiency of stochastic analysis. This paper presents a framework to investigate the influence and runout distance of rainfall-induced landslides w

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Xu, Wenqin, Chen Chen, Shuxin Ding, and Panos M. Pardalos. "A bi-objective dynamic collaborative task assignment under uncertainty using modified MOEA/D with heuristic initialization." Expert Systems with Applications 140 (February 2020): 112844. http://dx.doi.org/10.1016/j.eswa.2019.112844.

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Snyder, Ralph D., and Grant R. Saligari. "INITIALIZATION OF THE KALMAN FILTER WITH PARTIALLY DIFFUSE INITIAL CONDITIONS." Journal of Time Series Analysis 17, no. 4 (1996): 409–24. http://dx.doi.org/10.1111/j.1467-9892.1996.tb00285.x.

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Sörensson, Anna A., and Ernesto Hugo Berbery. "A Note on Soil Moisture Memory and Interactions with Surface Climate for Different Vegetation Types in the La Plata Basin." Journal of Hydrometeorology 16, no. 2 (2015): 716–29. http://dx.doi.org/10.1175/jhm-d-14-0102.1.

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Abstract This work examines the evolution of soil moisture initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata basin in South America. WRF–Noah simulations covering multiple cases during a 2-yr period are designed to emphasize the conceptual nature of the simulations at the expense of the statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial soil moisture differences

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Kushner, Paul J., Lawrence R. Mudryk, William Merryfield, et al. "Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system." Cryosphere 12, no. 4 (2018): 1137–56. http://dx.doi.org/10.5194/tc-12-1137-2018.

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Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with

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Solano-Carrillo, Edgardo. "Can a Single Neuron Learn Predictive Uncertainty?" International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 31, no. 03 (2023): 471–95. http://dx.doi.org/10.1142/s021848852350023x.

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Uncertainty estimation methods using deep learning approaches strive against separating how uncertain the state of the world manifests to us via measurement (objective end) from the way this gets scrambled with the model specification and training procedure used to predict such state (subjective means) — e.g., number of neurons, depth, connections, priors (if the model is bayesian), weight initialization, etc. This poses the question of the extent to which one can eliminate the degrees of freedom associated with these specifications and still being able to capture the objective end. Here, a no

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Ajami, H., J. P. Evans, M. F. McCabe, and S. Stisen. "Technical Note: Reducing the spin-up time of integrated surface water–groundwater models." Hydrology and Earth System Sciences 18, no. 12 (2014): 5169–79. http://dx.doi.org/10.5194/hess-18-5169-2014.

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Abstract. One of the main challenges in the application of coupled or integrated hydrologic models is specifying a catchment's initial conditions in terms of soil moisture and depth-to-water table (DTWT) distributions. One approach to reducing uncertainty in model initialization is to run the model recursively using either a single year or multiple years of forcing data until the system equilibrates with respect to state and diagnostic variables. However, such "spin-up" approaches often require many years of simulations, making them computationally intensive. In this study, a new hybrid approa

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Wang, Junwei, Wei Zhou, Yujun Tang, and Hanming Guo. "Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching." Applied Sciences 14, no. 18 (2024): 8457. http://dx.doi.org/10.3390/app14188457.

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After significant progress in stereo matching, the pursuit of robust and efficient ill-posed-region disparity refinement methods remains challenging. To further improve the performance of disparity refinement, in this paper, we propose the matchability and uncertainty-aware iterative disparity refinement neural network. Firstly, a new matchability and uncertainty decoder (MUD) is proposed to decode the matchability mask and disparity uncertainties, which are used to evaluate the reliability of feature matching and estimated disparity, thereby reducing the susceptibility to mismatched pixels. T

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Xu, Yanping, Tingcong Ye, Xin Wang, et al. "GMM with parameters initialization based on SVD for network threat detection." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 477–90. http://dx.doi.org/10.3233/jifs-200066.

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In the field of security, the data labels are unknown or the labels are too expensive to label, so that clustering methods are used to detect the threat behavior contained in the big data. The most widely used probabilistic clustering model is Gaussian Mixture Models(GMM), which is flexible and powerful to apply prior knowledge for modelling the uncertainty of the data. Therefore, in this paper, we use GMM to build the threat behavior detection model. Commonly, Expectation Maximization (EM) and Variational Inference (VI) are used to estimate the optimal parameters of GMM. However, both EM and

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Sauter, T., and F. Obleitner. "Assessment of the uncertainty of snowpack simulations based on variance decomposition." Geoscientific Model Development Discussions 8, no. 3 (2015): 2807–45. http://dx.doi.org/10.5194/gmdd-8-2807-2015.

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Abstract. State of the art numerical snow models essentially rely on observational data for initialization, forcing, parametrization and validation. Such data are available in increasing amount, but the inherent propagation of related uncertainties on the simulation results has received rather limited attention so far. Depending on their complexity, even small errors can have a profound effect on simulations, which dilutes our confidence in the results. This paper quantifies the fractional contributions of some archetypical measurement uncertainties on key simulation results in a high Arctic e

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Glasheen, Katherine, John Bird, and Eric Frew. "Experimental Assessment of Chance-Constrained Motion Planning for Small Uncrewed Aircraft." Field Robotics 4, no. 1 (2024): 70–98. http://dx.doi.org/10.55417/fr.2024003.

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This work extends the experimental evaluation of chance-constrained motion planning algorithms to fielded fixed-wing small uncrewed aircraft systems (sUAS). Despite advances in planning algorithms, certain challenges remain to producing trajectories for nonholonomic mobile robotic systems such as sUAS. These challenges include nonlinear dynamics which create a complex mapping from inputs to outputs, initialization uncertainty in online motion planning due to compute time of planners and latency in data transfer, environmental uncertainty that has non-Gaussian impact on the robot’s trajectory,

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Luo, Z., E. Wang, H. Zheng, J. A. Baldock, O. J. Sun, and Q. Shao. "Convergent modelling of past soil organic carbon stocks but divergent projections." Biogeosciences 12, no. 14 (2015): 4373–83. http://dx.doi.org/10.5194/bg-12-4373-2015.

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Abstract. Soil carbon (C) models are important tools for understanding soil C balance and projecting C stocks in terrestrial ecosystems, particularly under global change. The initialization and/or parameterization of soil C models can vary among studies even when the same model and data set are used, causing potential uncertainties in projections. Although a few studies have assessed such uncertainties, it is yet unclear what these uncertainties are correlated with and how they change across varying environmental and management conditions. Here, applying a process-based biogeochemical model to

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Cheng, Xiaoyu, Shanshan Liu, Wei He, et al. "A Model for Flywheel Fault Diagnosis Based on Fuzzy Fault Tree Analysis and Belief Rule Base." Machines 10, no. 2 (2022): 73. http://dx.doi.org/10.3390/machines10020073.

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In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree an

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Truong, Hung Quoc, Long Thanh Ngo, and Long The Pham. "Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (2019): 592–601. http://dx.doi.org/10.20965/jaciii.2019.p0592.

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The interval type-2 fuzzy possibilistic C-means clustering (IT2FPCM) algorithm improves the performance of the fuzzy possibilistic C-means clustering (FPCM) algorithm by addressing high degrees of noise and uncertainty. However, the IT2FPCM algorithm continues to face drawbacks including sensitivity to cluster centroid initialization, slow processing speed, and the possibility of being easily trapped in local optima. To overcome these drawbacks and better address noise and uncertainty, we propose an IT2FPCM method based on granular gravitational forces and particle swarm optimization (PSO). Th

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Ađalgeirsdóttir, G., A. Aschwanden, C. Khroulev, et al. "Role of model initialization for projections of 21st-century Greenland ice sheet mass loss." Journal of Glaciology 60, no. 222 (2014): 782–94. http://dx.doi.org/10.3189/2014jog13j202.

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AbstractModel simulations of the Greenland ice sheet contribution to 21st-century sea-level rise are performed with a state-of-the-art ice-sheet model (Parallel Ice Sheet Model (PISM)). The climate-forcing fields are obtained from the European Union’s Seventh Framework Programme project ice2sea, in which three regional climate models are used to dynamically downscale two scenarios (A1B and E1) from two general circulation models (ECHAM5 and HadCM3). To assess the sensitivity of the projections to the model initial state, four initialization methods are applied. In these experiments, the simula

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Auligné, Thomas, Benjamin Ménétrier, Andrew C. Lorenc, and Mark Buehner. "Ensemble–Variational Integrated Localized Data Assimilation." Monthly Weather Review 144, no. 10 (2016): 3677–96. http://dx.doi.org/10.1175/mwr-d-15-0252.1.

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Hybrid variational–ensemble data assimilation (hybrid DA) is widely used in research and operational systems, and it is considered the current state of the art for the initialization of numerical weather prediction models. However, hybrid DA requires a separate ensemble DA to estimate the uncertainty in the deterministic variational DA, which can be suboptimal both technically and scientifically. A new framework called the ensemble–variational integrated localized (EVIL) data assimilation addresses this inconvenience by updating the ensemble analyses using information from the variational dete

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Lee, Taehoon, Byungjin Lee, and Sangkyung Sung. "Invariant Kalman Filter Design for Securing Robust Performance of Magnetic–Inertial Integrated Navigation System under Measurement Uncertainty." Aerospace 11, no. 4 (2024): 268. http://dx.doi.org/10.3390/aerospace11040268.

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This study proposes an enhanced integration algorithm that combines the magnetic field-based positioning system (MPS—Magnetic Pose Estimation System) with an inertial system with the advantage of an invariant filter structure. Specifically, to mitigate the nonlinearity of the propagation model and perturbing effect from the estimated uncertainty, the formulation of the invariant Kalman filter was derived in detail. Then, experiments were conducted to validate the algorithm with an unmanned vehicle equipped with an IMU and MPS receiver. As a result, the navigation performance of the IEKF-based

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Rusanova, Svitlana, and Maja Perepichko. "FACTORS FORMING THE SUCCESS OF PILOT PROJECTS: FROM INITIALIZATION TO RISK ASSESSMENT." Development of Management and Entrepreneurship Methods on Transport (ONMU) 87, no. 2 (2024): 148–57. https://doi.org/10.31375/2226-1915-2024-2-148-157.

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In view of the destroyed cargo delivery routes in Ukraine, many companies are forced to quickly change the geography of production and directions of trade flows. Previously established supply chains ceased to function. Many new supply chains have become subject to considerable uncertainty. Random events of various kinds disrupt the coordinated operation of the system and create the risk of not receiving the expected results. Now there is a need to develop new experimental logistic ways of so-called pilot projects. Such projects are created to test the viability of the proposed solution. The in

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Luo, Z., E. Wang, H. Zheng, J. A. Baldock, O. J. Sun, and Q. Shao. "Convergent modeling of past soil organic carbon stocks but divergent projections." Biogeosciences Discussions 12, no. 5 (2015): 4245–72. http://dx.doi.org/10.5194/bgd-12-4245-2015.

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Abstract. Soil carbon models are important tool to understand soil carbon balance and project carbon stocks in terrestrial ecosystems, particularly under global change. The initialization and/or parameterization of soil carbon models can vary among studies even when the same model and dataset are used, causing potential uncertainties in projections. Although a few studies have assessed such uncertainties, it is yet unclear what these uncertainties are correlated with and how they change across varying environmental and management conditions. Here, applying a process-based biogeochemical model

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Lucas, Donald D., Matthew Simpson, Philip Cameron-Smith, and Ronald L. Baskett. "Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant." Atmospheric Chemistry and Physics 17, no. 22 (2017): 13521–43. http://dx.doi.org/10.5194/acp-17-13521-2017.

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Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initia

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Huang, Min, Gregory R. Carmichael, James H. Crawford, et al. "Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns." Geoscientific Model Development 10, no. 8 (2017): 3085–104. http://dx.doi.org/10.5194/gmd-10-3085-2017.

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Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned late

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