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218 results for “Physical Modelling”
Coupling deep learning and physically-based hydrological models for monthly streamflow predictions
<p>Revision in journal Water Resources Research, Manuscript number: <strong><span>2023WR035618R</span></strong></p> <p><strong>Abstract:</strong><strong> </strong>This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically-based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. <span>It</span> is then driven by bias corrected general circulation model (GCM) <span>prediction</span>s to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future <span>monthly </span>streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead <span>reservoir </span>inflows <span>for the Danjiangkou Reservoir in China. </span>The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. <span>Additional tests indicate that hybrid</span> model<span>s based on CNN-GRU </span>outperform <span>those based on</span> <span>LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model</span> greatly reduce<span>s</span> the <span>computation </span>burden of rolling prediction<span>. It also </span>saves decision-makers the time and effort of trying different combinations of predictors<span>, which is indispensable when building DL models. Overall</span>, the new hybrid model <span>demonstrates great potential</span> for monthly streamflow prediction <span>where</span> training data are limited.</p> <p><strong><span>Keywords:</span></strong> <span>monthly streamflow prediction; deep learning; </span><span>physically-based distributed hydrological model; </span><span>VIC model; soil moisture; hybrid model </span></p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
Data for "Improved simulation of Madden–Julian Oscillation with the modified moist physical parameterizations for a global climate model"
<p>Model output data for the manuscript "Improved simulation of Madden–Julian Oscillation with the modified moist physical parameterizations for a global climate model", including convective precipitation (PRECC), large-scale precipitation (PRECL), mean SST and U850, <span>column-integrated MSE tendency anomalies, and boundary layer moisture convergence. </span></p>
Code and datasets of Socio-environmental modelling shows physics-like confidence with water modelling surpassing it in numerical claims
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Rock physics models of gas hydrate bearing sediments – the classification, simulation workflow, and challenges
<p>This study reviews the rock physics models for simulating the elastic properties of gas hydrate bearing sediments. Considering that it is confusing to select the appropriate model for a specific study from the various models, we classify the models into five categories according to different principles. We also summarize a general workflow of the modeling process, elaborate the possible models in each step and bring up the potential sources of uncertainties. Besides, we explicate the general problems of the current models and raise several potential research directions. This study provides us a clear view of the rock physics models, the associated uncertainties, as well as the general modeling workflow of gas hydrate bearing sediments, and also provides some implications for future studies.</p>
Wave-current Coupling Effects on the Variation Modes of Pore Pressure Response in a Sandy Seabed: Physical Modeling and Explicit Approximations
<p>This is experimental data of combined wave-current induced pore pressure. The corresponding test condition is given in the title of each excel. The channels 4, 2, 1 represent the wave height data measured by the WHGs just above PPTs, in the upstream, and in the downstream, respectively. The channels 6, 8, 3, 7 are pore pressure data monitored by PPTs installed at 0, 6, 9, and 15 cm below the seabed surface, respectively.</p>
The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
<p>Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.</p>
Geometric and Physical Reduced Order Modeling Applied to CFD Results
<p>Short videos illustrating the results from the ROM paper</p>
Data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling"
<p>This is a data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling."</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
Dataset related to the paper submitted in Journal of Geophysical Research : Solid Earth, named : A Controlled-Source Physical Model for Long Period Events
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Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"
<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>
PhysicsGen - Can Generative Models Learn from Images to Predict Complex Physical Relations?
<p>This dataset comprises 300,000 pairs of images designed for the advancement of generative model applications in physical simulations. Each pair consists of an input image and its corresponding output image that represents a physical simulation. The dataset aims to facilitate research into whether generative models can effectively learn and reproduce complex physical dynamics from visual data, potentially replacing traditional differential equation-based methods with significant computational speedups.</p> <p>Data, baseline models and evaluation code: <a href="https://www.physics-gen.org">https://www.physics-gen.org</a></p>
Figures and Data for "CO2 rock physics modeling for reliable monitoring of geologic carbon storage"
<p>The following data includes all data used to generate figures in this study. We will update the link to the paper once it is published. It has been accepted in Nature Comm Earth and Environment. LANL has approved this release: LA-UR-24-25434.</p>
Trained Random Forest model and scaler parameters on new physical and tsfel features from seismic data of 150s length.
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A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
<p>This dataset accompanies the GMD article 'A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0' (https://doi.org/10.5194/egusphere-2024-96).</p> <ul> <li>The input files to run the presented cases using uDALES are contained in 'inputs'.</li> <li>The model outputs are contained in separate folders: 'XCC', 'indoor-outdoor', 'XCB', and 'SEB'. When downloaded, move into a folder called 'outputs' so that the paths defined in the scripts work as intended (see below).</li> <li>The Matlab scripts to plot the figures are contained in 'scripts'.</li> <li>The figures shown in the article are contained in 'figures'.</li> </ul>
Data for Modeling Snow on Sea Ice using Physics Guided Machine Learning
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Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
Model input for initial submission of "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to GMD
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in the initual submission "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to Geoscientific Model Development.</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information:https://doi.org/10.48670/moi-00021. Refer to the manuscript for references to other data sources.</p> <p>Codes for generating regional MOM6 initial conditions, boundary conditions and other necessary model inputs as well as diagnostic scripts are maintained on the NOAA CEFI GitHub Repository: https://github.com/NOAA-GFDL/CEFI-regional-MOM6/. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.