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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Probing Molecular Packing of Amorphous Pharmaceutical Solids Using X-ray Atomic Pair Distribution Function and Solid-State NMR

The structural investigation of amorphous pharmaceuticals is of paramount importance in comprehending their physicochemical stability. However, it has remained a relatively underexplored realm primarily due to the limited availability of high-resolution analytical tools. Here in this study, we utilized the combined power of X-ray pair distribution functions (PDFs) and solid-state nuclear magnetic resonance (ssNMR) techniques to probe the molecular packing of amorphous posaconazole and its amorphous solid dispersion at the molecular level. Leveraging synchrotron X-ray PDF data and employing the empirical potential structure refinement (EPSR) methodology, we unraveled the existence of a rigid conformation and discerned short-range intermolecular C–F contacts within amorphous posaconazole. Encouragingly, our ssNMR 19 F– 13 C distance measurements offered corroborative evidence supporting these findings. Furthermore, employing principal component analysis on the X-ray PDF and ssNMR data sets enabled us to gain invaluable insights into the chemical nature of the intermolecular interactions governing the drug–polymer interplay. These outcomes not only furnish crucial structural insights facilitating the comprehension of the underlying mechanisms governing the physicochemical stability but also underscore the efficacy of synergistically harnessing X-ray PDF and ssNMR techniques, complemented by robust modeling strategies, to achieve a high-resolution exploration of amorphous structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

An Empirical Cumulus Parameterization Scheme for a Global Spectral Model

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.

Rajendran, K.↗
NASA NTRS1994

Passive microwave observations of the Wedell Sea during austral winter and early spring

The results of multispectral passive microwave observations (6.7 to 90-GHz) are presented from the cruises of the FS Polarstern in the Weddell Sea from July to December 1986. This paper includes primarily the analysis of radiometric observations taken at ice station sites. Averaged emissivity spectra for first-year (FY) ice were relatively constant throughout the experiment and were not statistically different from FY ice signatures in the Arctic. Detailed ice characterization was carried out at each site to compare the microwave signatures of the ice with the physical properties. Absorption optical depths of FY ice were found to be sufficiently high that only the structure in the upper portions of the ice contributed significantly to interstation emissivity variations. The emissivities at 90-GHz, e(90), had the greatest variance. Both e(90) at vertical polarization and GR(sub e)(90, 18.7)(defined as (e(sub V)(90)-e(sub V)(18.7))/e(sub V)(90 + e(sub V)(18.7)) depended on the scattering optical depth which is a function of the snow grain diameter and layer thickness. The variance showed a latitude dependence and is probably due to an increase in the strength of snow metamorphism nearer the northern edge of the ice pack. The contribution of variations of near-surface brine volume to the emissivity was not significant over the range of values encountered at the station sites. Emissivity spectra are presented for a range of thin ice types. Unsupervised principal component analysis produced three significant eigenvectors and showed a separation among four different surface types: open water, thin ice, FY ice, and FY ice with a thick snow cover. A comparison with SMMR satellite data showed that average ice concentrations derived from the ship's ice watch log were consistent with the satellite concentrations. The surface based emissivities for FY ice were also compared with emissivities calculated from scanning multichannel microwave radiometer (SMMR) satellite radiances. Best agreement was found at 6.7 and 10-GHz, while at 18 and 37-GHz, SMMR emissivities were slightly lower than surface based results. For the three lower frequencies agreement was found within a confidence limit of 95% and for 37-GHz within about 90%.

Grenfell, T. C.↗
NASA NTRS2012

Seasonal Variability of Saturn's Atmosphere

The seasonal variability of Saturn's clouds and weather layer, currently displaying a variety of phenomena (convective storms, planetary waves, giant storms and lightning-induced events, etc.) is not yet fully understood. Variations of Saturn's radiance at 5.2 microns, a spectral region dominated by thermal emission in an atmospheric window containing weak gaseous absorption, contain a strong axisymmetric component as well as large discrete features at low and mid-latitudes that are several degrees colder than the planetary average and uncorrelated with features at shorter wavelengths that are dominated by reflected sunlight (Yanamandra-Fisher et al., 2001. Icarus, Vol. 150). The characterization of several fundamental atmospheric properties and processes, however, remains incomplete, namely: How do seasons affect (a) the global distribution of gaseous constituents and aerosols; and (b) temperatures and the stability against convection and large scale-atmospheric transport? Do 5-micron clouds have counterparts at other altitude levels? What changes occur during the emergence of Great White Storms? Data acquired at the NASA/IRTF and NAOJ/Subaru from 1995 - 2011; since 2004, high-resolution multi-spectral and high-spatial imaging data acquired by the NASA/ESA Cassini mission, represents half a Saturnian year or two seasons. With the addition of detailed multi-spectral data sets acquired by amateur observers, we study these dramatic phenomena to better understand the timeline of the evolution of these events. Seasonal (or temporal) trends in the observables such as albedo of the clouds, thermal fields of the atmosphere as function of altitude, development of clouds, hazes and global abundances of various hydrocarbons in the atmosphere can now be modeled. We will present results of our ongoing investigation for the search and characterization of periodicities over half a Saturnian year, based on a non-biased a priori approach and time series techniques (such as Principal Component Analysis, PCA and Lomb-Scargle periodograms, LSP).

Yanamandra-Fisher, Padma A.↗
DOE OSTI2024

Quarterly Research Performance Progress Report (Q8)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. Once satisfied with the results we have started by targeting the modeling of the DFITs at conducted at well 58-32. We have a identified a specific test (cycle 4 in zone 2) as the most interesting to be model with GEOS hydraulic fracturing module. Thus, we have first produced results with an isothermal model and adjusted model parameters to get a satisfying match with field pressure data. The, we have added thermal effects and compared the modeling results with and without thermal effects to estimate how thermal effects may influence test interpretation. Models seem to suggest that, for small volumes of fluid, thermal effects are moderate. In Task 2, we have adapted GEOS phase-field formulation to be able to simulate near-wellbore hydraulic fracture nucleation and propagation. We have devised a novel formulation that, compared to other existing ones, incorporates rock strengths. We have submitted a journal publication about our work. We are currently employing this phase-field formulation to model the experiments taking place at U Pitt and help us understand the effect of various parameters. In Task 3, we have built a model of the region surround well 16A and have started modeling stage 3 stimulation because of its simpler planar geometry. After calibrating simulation parameters using known analytical solutions, we have simulated the stage 3 stimulation using our isothermal hydraulic fracturing module, varying the permeability field, the stress conditions including different physics to get a better understanding of the numerical challenges and of the effects of varying these parameters on the simulation results. In Task 4 laboratory experimentation, a set of specialized drilling and injection tools has been customized and constructed to accommodate an inclined well with an orientation of up to 30 degrees relative to material anisotropy or principal stress axes. These inclined samples have also undergone thermal stress and hydraulic fracturing at a temperature of 190 degrees Celsius. Furthermore, both vertical and deviated sampling testing setups enable an extended analysis of post-peak pressure behaviors, facilitating post-test pressure analyses such as the G-function, step rate, and fracture reopening measurements. Thus, the key components of in-situ stress estimation can be extracted and validated through our experiment, providing a solid foundation for validating existing in-situ stress estimation theories or proposing new ones. Simultaneously, we are integrating computer vision techniques with traditional experimental fracture observation methods such as multi-overcore/slicing and water-penetration fracture observation. This combination will prove beneficial in populating the hydraulic fracture patterns database, generated under challenging EGS conditions. This approach aims to deepen our understanding of the complexities in EGS reservoirs and pave the way for future data-driven investigations. Additionally, PITT has also equipped the ELE International compression machine, which is now prepared for conducting indirect tensile and fracture toughness tests. These tests will aid in characterizing how rock fabrics influence the resulting fracture patterns. Additionally, we have completed the required personnel training and gained access to Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for conducting more detailed characterization and analysis of rock fabrics, as well as the examination of thermal and hydraulically induced fracture patterns. Thus, the PITT team has effectively demonstrated the capabilities of our experimental apparatuses in exploring the thermal effects, well deviation angles, material anisotropy, and operational choices (such as circulation rate, injection fluid viscosity, and injection rate) and their impact on pressure responses and fracture trajectories under the Utah FORGE conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Machine learning prediction of the mechanical properties of refractory multicomponent alloys based on a dataset of phase and first principles simulation

In this work, a dataset including structural and mechanical properties of refractory multicomponent alloys was developed by fusing computations of phase diagram (CALPHAD) and density functional theory (DFT). The refractory multicomponent alloys, also named refractory complex concentrated alloys (CCAs) which contain 2–5 types of refractory elements were constructed based on Special Quasi-random Structure (SQS). The phase of alloys was predicted using CALPHAD and the mechanical property of alloys with stable and single body-centered cubic (BCC) at high temperature (over 1,500°C) was investigated using DFT-based simulation. As a result, a dataset with 393 refractory alloys and 12 features, including volume, melting temperature, density, energy, elastic constants, mechanical moduli, and hardness, were produced. To test the capability of the dataset on supporting machine learning (ML) study to investigate the property of CCAs, CALPHAD, and DFT calculations were compared with principal components analysis (PCA) technique and rule of mixture (ROM), respectively. It is demonstrated that the CALPHAD and DFT results are more in line with experimental observations for the alloy phase, structural and mechanical properties. Furthermore, the data were utilized to train a verity of ML models to predict the performance of certain CCAs with advanced mechanical properties, highlighting the usefulness of the dataset for ML technique on CCA property prediction.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Tools for Performing SBG hyperspectral Observing System Simulation Experiment

One of NASA’s Decadal Survey mission, Surface Biology and Geology (SBG), will include a hyperspectral remote sensing imager, which has a very high spatial resolution and a wide spectral coverage (from UV to Near IR). Unprecedented large data volumes will be generated by the SBG hyperspectral instrument. Before the launch of the new satellite, an Observing System Simulation Experiment (OSSE) can be used to study different designs of the new satellite system. One of the key components in an OSSE study is a radiative transfer model (RTM) or forward model. In this presentation, we will describe a Principal Component-based Radiative Transfer Model (PCRTM) which is capable of simulating atmospheric (TOA) radiance or reflectance spectra from far IR to visible and UV spectral regions (50 wavenumber to 30000 wavenumber) quickly and accurately. Multiple scattering from multiple layers of clouds/aerosols are included in the model. The PCRTM has a very good accuracy relative to reference line-by-line radiative transfer models (LBLRTM), and it saves 3-4 orders of magnitude computational time relative to LBLRTM or MODTRAN. The PCRTM model has been successfully used to analyze large volumes of data from hyperspectral sensors such as AIRS, CrIS, and IASI. It has also been used to perform OSSE studies for the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission. Another useful tool for the OSSE is surface Bidirectional Reflectance Distribution Function (BRDF) database. It is very crucial for the SBG OSSE to include realistic BRDF spectra. Currently, most of the surface reflectance spectra such as those in the ECOSIS and ECOSTRESS are measured at specific observation geometries. We have developed a hyperspectral bidirectional reflectance (HSBR) model which combines Ross-Li BRDF model with the existing reflectance spectral libraries using a principal component analysis. This HSBR model can provide realistic BRDF spectra under various observation conditions. It can also be used to generate realistic BRDF spectra using measurements from multi-band imagers or spectrometers such as MODIS or VIIRS.

Xu Liu↗
DOE OSTI2022

Variational quantum state eigensolver

Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important application of near-term quantum computers. The variational quantum eigensolver (VQE) treats the case when the matrix is a Hamiltonian. Here, we address the case when the matrix is a density matrix ρ. We introduce the variational quantum state eigensolver (VQSE), which is analogous to VQE in that it variationally learns the largest eigenvalues of ρ as well as a gate sequence V that prepares the corresponding eigenvectors. VQSE exploits the connection between diagonalization and majorization to define a cost function C=Tr(ρ~H) where H is a non-degenerate Hamiltonian. Due to Schur-concavity, C is minimized when ρ~=VρV† is diagonal in the eigenbasis of H. VQSE only requires a single copy of ρ (only n qubits) per iteration of the VQSE algorithm, making it amenable for near-term implementation. We heuristically demonstrate two applications of VQSE: (1) Principal component analysis, and (2) Error mitigation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks

For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, are a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analyses with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA-/PCA-based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine, a priori, the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates, which are nonlinear functions of the reconstructed state, as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: zero-dimensional (homogeneous) reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage (low and high temperature) ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional dataset representing turbulent autoignition of ethanol in a homogeneous charge compression ignition (HCCI) engine. Finally, results from the analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Model orthogonalization and Bayesian forecast mixing via principal component analysis

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this paper we describe a method based on the principal component analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian model combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Cardiovascular group

As a starting point, the group defined a primary goal of maintaining in flight a level of systemic oxygen transport capacity comparable to each individual's preflight upright baseline. The goal of maintaining capacity at preflight levels would seem to be a reasonable objective for several different reasons, including the maintenance of good health in general and the preservation of sufficient cardiovascular reserve capacity to meet operational demands. It is also important not to introduce confounding variables in whatever other physiological studies are being performed. A change in the level of fitness is likely to be a significant confounding variable in the study of many organ systems. The principal component of the in-flight cardiovascular exercise program should be large-muscle activity such as treadmill exercise. It is desirable that at least one session per week be monitored to assure maintenance of proper functional levels and to provide guidance for any adjustments of the exercise prescription. Appropriate measurements include evaluation of the heart-rate/workload or the heart-rate/oxygen-uptake relationship. Respiratory gas analysis is helpful by providing better opportunities to document relative workload levels from analysis of the interrelationships among VO2, VCO2, and ventilation. The committee felt that there is no clear evidence that any particular in-flight exercise regimen is protective against orthostatic hypotension during the early readaptation phase. Some group members suggested that maintenance of the lower body muscle mass and muscle tone may be helpful. There is also evidence that late in-flight interventions to reexpand blood volume to preflight levels are helpful in preventing or minimizing postflight orthostatic hypotension.

Blomqvist, Gunnar↗
NASA NTRS2010

Forced and Unforced Variability of Twentieth Century North American Droughts and Pluvials

Research on the forcing of drought and pluvial events over North America is dominated by general circulation model experiments that often have operational limitations (e.g., computational expense, ability to simulate relevant processes, etc). We use a statistically based modeling approach to investigate sea surface temperature (SST) forcing of the twentieth century pluvial (1905-1917) and drought (1932-1939, 1948-1957, 1998-2002) events. A principal component (PC) analysis of Palmer Drought Severity Index (PDSI) from the North American Drought Atlas separates the drought variability into five leading modes accounting for 62% of the underlying variance. Over the full period spanning these events (1900-2005), the first three PCs significantly correlate with SSTs in the equatorial Pacific (PC 1), North Pacific (PC 2), and North Atlantic (PC 3), with spatial patterns (as defined by the empirical orthogonal functions) consistent with our understanding of North American drought responses to SST forcing. We use a large ensemble statistical modeling approach to determine how successfully we can reproduce these drought/pluvial events using these three modes of variability. Using Pacific forcing only (PCs 1-2), we are able to reproduce the 1948-1957 drought and 1905-1917 pluvial above a 95% random noise threshold in over 90% of the ensemble members; the addition of Atlantic forcing (PCs 1-2-3) provides only marginal improvement. For the 1998-2002 drought, Pacific forcing reproduces the drought above noise in over 65% of the ensemble members, with the addition of Atlantic forcing increasing the number passing to over 80%. The severity of the drought, however, is underestimated in the ensemble median, suggesting this drought intensity can only be achieved through internal variability or other processes. Pacific only forcing does a poor job of reproducing the 1932-1939 drought pattern in the ensemble median, and less than one third of ensemble members exceed the noise threshold (28%). Inclusion of Atlantic forcing improves the ensemble median drought pattern and nearly doubles the number of ensemble members passing the noise threshold (52%). Even with the inclusion of Atlantic forcing, the intensity of the simulated 1932-1939 drought is muted, and the drought itself extends too far into the southwest and southern Great Plains. To an even greater extent than the 1998-2002 drought, these results suggest much of the variance in the 1932-1939 drought is dependent on processes other than SST forcing. This study highlights the importance of internal noise and non SST processes for hydroclimatic variability over North America, complementing existing research using general circulation models.

Cook, Benjamin I.↗
NASA NTRS1984

Preliminary Evaluation of Thematic Mapper Image Data Quality

Thematic Mapper (TM) data from Mississippi County, Arkansas, and Webster County, Iowa, were examined for the purpose of evaluating the image data quality of the TM which was launched on board the LANDSAT-4 spacecraft. Preliminary clustering and principal component analysis indicates that the middle infrared and thermal infrared data of TM appear to add significant information over that of the near IR and visible bands of the multispectral scanner data. Moreover, the higher spatial resolution of TM appears to provide better definition of the edges and the within variability of agricultural fields. The geometric performance of TM data, without ground control correction, was found to exceed expectations. The modulation transfer function for the 1.65 m band was found to agree with prelaunch specifications when the effects of the GSFC cubic convolution and the atmosphere were removed. The band to band registration for the bands within the noncooled focal plane was found to be better than specified. However, the middle infrared and thermal infrared, which are on a separate cooled focal plane were found to be misregistered and were significantly worse than prelaunch specifications.

Macdonald, R. B.↗
DOE OSTI2024

Lipid droplet-associated proteins in alcohol-associated fatty liver disease: A proteomic approach

The earliest manifestation of alcohol-associated liver disease (ALD) is steatosis characterized by deposition of fat in specialized organelles called lipid droplets (LDs). While alcohol administration causes a rise in LD numbers in the hepatocytes, little is known regarding their characteristics that allow their accumulation and size to increase. The aim of the present study is to gain insights into underlying pathophysiological mechanisms by investigating the ethanol-induced changes in hepatic LD proteome as a function of LD size. Adult male Wistar rats (180–200 g BW) were fed with ethanol liquid diet for 6 weeks. At sacrifice, large-, medium-, and small-sized hepatic LD subpopulations (LD1, LD2, and LD3, respectively) were isolated and subjected to morphological and proteomic analyses. Morphological analysis of LD1-LD3 fractions of ethanol-fed rats clearly demonstrated that LD1 contained larger LDs compared with LD2 and LD3 fractions. Our preliminary results from principal component analysis showed that the proteome of different-sized hepatic LD fractions was distinctly different. Proteomic data analysis identified over 2000 proteins in each LD fraction with significant alterations in protein abundance among the three LD fractions. Among the altered proteins, several were related to fat metabolism, including synthesis, incorporation of fatty acid, and lipolysis. Ingenuity pathway analysis revealed increased fatty acid synthesis, fatty acid incorporation, LD fusion, and reduced lipolysis in LD1 compared to LD3. Overall, the proteomic findings indicate that the increased level of protein that facilitates fusion of LDs combined with an increased association of negative regulators of lipolysis dictates the generation of large-sized LDs during the development of alcohol-associated hepatic steatosis. Several significantly altered proteins were identified in different-sized LDs isolated from livers of ethanol-fed rats. Ethanol-induced increases in specific proteins that hinder LD lipid metabolism led to the accumulation and persistence of large-sized LDs in the liver.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1979

Principal component analysis of solar flares in the soft X-ray flux

The paper considers principal component analysis of solar flares in the soft X-ray flux, a technique for extracting the salient features from a mass of data. The method applies particularly to the analysis of nonstationary ensembles, and its computations require the evaluation of eigenvalues of matrices. The Eispack matrix eigen system routines were used to analyze full-disk proportional-counter data from the X-ray event analyzer which was part of the Skylab experiment. Empirical orthogonal functions were derived for events in the soft X-ray spectrum between 2.5 and 20 A during different time periods, indicating that about 90% of the cumulative power of each analyzed flare is contained in the largest eigenvector. The first two largest eigenvectors are sufficient for an empirical curve fit through the raw data and a characterization of solar flares in the soft X-ray flux, and power spectra of two largest eigenvectors reveal a reported periodicity of about 5 min.

Teuber, D. L.↗
DOE OSTI2024

Quantifying Drivers of Methane Hydrobiogeochemistry in a Tidal River Floodplain System

The influence of coastal ecosystems on global greenhouse gas (GHG) budgets and their response to increasing inundation and salinization remains poorly constrained. In this study, we have integrated an uncertainty quantification (UQ) and ensemble machine learning (ML) framework to identify and rank the most influential processes, properties, and conditions controlling methane behavior in a freshwater floodplain responding to recently restored seawater inundation. Our unique multivariate, multiyear, and multi-site dataset comprises tidal creek and floodplain porewater observations encompassing water level, salinity, pH, temperature, dissolved oxygen (DO), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), partial pressure of carbon dioxide (pCO 2 ), nitrous oxide (pN 2 O), methane (pCH 4 ), and the stable isotopic composition of methane (δ 13 CH 4 ). Additionally, we incorporated topographical data, soil porosity, hydraulic conductivity, and water retention parameters for UQ analysis using a previously developed 3D variably saturated flow and transport floodplain model for a physical mechanistic understanding of factors influencing groundwater levels and salinity and, therefore, CH 4 . Principal component analysis revealed that groundwater level and salinity are the most significant predictors of overall biogeochemical variability. The ensemble ML models and UQ analyses identified DO, water level, salinity, and temperature as the most influential factors for porewater methane levels and indicated that approximately 80% of the total variability in hourly water levels and around 60% of the total variability in hourly salinity can be explained by permeability, creek water level, and two van Genuchten water retention function parameters: the air-entry suction parameter α and the pore size distribution parameter m. These findings provide insights on the physicochemical factors in methane behavior in coastal ecosystems and their representation in local- to global-scale Earth system models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Optimal band selection for dimensionality reduction of hyperspectral imagery

Hyperspectral images have many bands requiring significant computational power for machine interpretation. During image pre-processing, regions of interest that warrant full examination need to be identified quickly. One technique for speeding up the processing is to use only a small subset of bands to determine the 'interesting' regions. The problem addressed here is how to determine the fewest bands required to achieve a specified performance goal for pixel classification. The band selection problem has been addressed previously Chen et al., Ghassemian et al., Henderson et al., and Kim et al.. Some popular techniques for reducing the dimensionality of a feature space, such as principal components analysis, reduce dimensionality by computing new features that are linear combinations of the original features. However, such approaches require measuring and processing all the available bands before the dimensionality is reduced. Our approach, adapted from previous multidimensional signal analysis research, is simpler and achieves dimensionality reduction by selecting bands. Feature selection algorithms are used to determine which combination of bands has the lowest probability of pixel misclassification. Two elements required by this approach are a choice of objective function and a choice of search strategy.

Stearns, Stephen D.↗
NASA NTRS2004

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Neural Networks (Computer)↗