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Dataset results
8 results for “Unsaturated soils”
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Field investigation of unsaturated seepage process and its influence on soil behavior for land creation in Loess plateau with fiber-optic technology
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Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
Hydraulic hysteresis of unsaturated pyroclastic soils: experimental investigation and model calibration
<p>In many geotechnical applications, especially in the study of weather-induced landslides, a reliable soil hydraulic characterization under unsaturated conditions is required. Currently, the experimental techniques neglecting hydraulic hysteresis represent the greatest limitation to landslide forecasting. Here, experimental data from a new procedure to obtain an unsaturated soil hydraulic characterization are reported. These allows us to evaluate the soil hydraulic properties not only along the main drying path but also along wetting/drying cycles. Pyroclastic soil samples collected at a test site located at Mount Faito in the Campania region (southern Italy) were tested. The experimental investigation consisted of a forced evaporation test followed by a number of wetting-drying cycles. </p>
data for "A novel three-phase interface premelting theory for determining unfrozen water content in unsaturated soil"
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Data and code used in the article "A deep learning method for predicting soil moisture in unsaturated areas based on physical constraints"
<p>Data and code used in the article "A deep learning method for predicting soil moisture in unsaturated areas based on physical constraints", specifically included are water content data from 55 in situ observations for the years 2018-2020 (observation frequency of 5min or 10min), and example code for implementing LSTM and PIDL using python (mainly the tensorflow library).These data can help the reader to better understand and replicate our research. All the data and code has been uploaded. </p><p>The paper has been published in <i>Water Resources Research</i>, and the citation is: </p><p>Wang, Y., Wang, W., Ma, Z., Zhao, M., Li, W., Hou, X., et al. (2023). A deep learning approach based on physical constraints for predicting soil moisture in unsaturated zones. <i>Water Resources Research</i>,<i>59</i>, e2023WR035194. https://doi.org/10.1029/2023WR035194</p>
Dataset for Journal Paper - Liquefaction assessment in unsaturated soils
<p>This is a dataset for the journal paper "Liquefaction assessment in unsaturated soils". The related article has been published in ASCE Journal of Geotechnical and Geoenvironmental Engineering. DOI: <a href="https://doi.org/10.1061/(ASCE)GT.1943-5606.0002851">10.1061/(ASCE)GT.1943-5606.0002851</a></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.