Spatial prediction of topsoil salinity in the Chelif Valley, Algeria, using local ordinary kriging with local variograms versus whole-area variogram

Soil Research ◽  
2001 ◽  
Vol 39 (2) ◽  
pp. 259 ◽  
Author(s):  
Christian Walter ◽  
Alex B. McBratney ◽  
Abdelkader Douaoui ◽  
Budiman Minasny
Keyword(s):  
Ordinary Kriging ◽  
Regional Scale ◽  
Large Data ◽  
Spatial Prediction ◽  
Uncertainty Assessment ◽  
Prediction Errors ◽  
Spatial Structures ◽  
Data Set ◽  
Salinity Hazard ◽  
Validation Set

A novel form of ordinary kriging, involving the local estimation and modelling of the variogram at each prediction site (OKLV), is tested at a regional scale on a large data set, in order to adapt to non-uniform spatial structures and improve the assessment of the salinity hazard in the lower Chelif Valley, Algeria. The spatial variability study was carried out on a 38000 ha area using 5141 topsoil electrical conductivity (EC) measurements systematically sampled on a 250 m by 250 m grid. Variography analysis confirmed the existence of large trends in the EC variability with differing spatial structures between sub-areas. OKLV performed better than ordinary kriging with a whole-area variogram (OKWV) in predicting the proportion of high saline soils in large blocks, but the predictions appeared mostly similar. In contrast, the estimation variance maps revealing the uncertainties of the spatial predictions were markedly different between the 2 methods. OKLV integrates the local spatial structure in the uncertainty assessment, whereas kriging with a whole-area variogram only considers the sampling intensity. Comparison with prediction errors on a validation set confirmed the consistency of the OKLV prediction variance. This appears to be a major improvement for decision-making procedures such as delineating areas where remediation should take place.

Helminth species diversity of mammals: parasite species richness is a host species attribute

Parasitology ◽  
2008 ◽  
Vol 135 (14) ◽  
pp. 1701-1705 ◽  
Author(s):  
F. BORDES ◽  
S. MORAND
Keyword(s):  
Species Richness ◽  
Host Species ◽  
Parasite Species ◽  
Regional Scale ◽  
Large Data ◽  
Helminth Species ◽  
Future Research ◽  
Data Set ◽  
Parasite Species Richness ◽  
Trade Offs

SUMMARYStudies investigating parasite diversity have shown substantial geographical variation in parasite species richness. Most of these studies have, however, adopted a local scale approach, which may have masked more general patterns. Recent studies have shown that ectoparasite species richness in mammals seems highly repeatable among populations of the same mammal host species at a regional scale. In light of these new studies we have reinvestigated the case of parasitic helminths by using a large data set of parasites from mammal populations in 3 continents. We collected homogeneous data and demonstrated that helminth species richness is highly repeatable in mammals at a regional scale. Our results highlight the strong influence of host identity in parasite species richness and call for future research linking helminth species found in a given host to its ecology, immune defences and potential energetic trade-offs.

scholarly journals Detrital zircon petrochronology of central Australia, and implications for the secular record of zircon trace element composition

Geosphere ◽  
2021 ◽  
Author(s):  
Charles Verdel ◽  
Matthew J. Campbell ◽  
Charlotte M. Allen
Keyword(s):  
Trace Element ◽  
Continental Crust ◽  
Detrital Zircon ◽  
Isotope Composition ◽  
Regional Scale ◽  
Large Data ◽  
Hf Isotope ◽  
Geological Time ◽  
Related Factors ◽  
Data Set

Hafnium (Hf) isotope composition of zircon has been integrated with U-Pb age to form a long-term (>4 b.y.) record of the evolution of the crust. In contrast, trace element compositions of zircon are most commonly utilized in local- or regional-scale petrological studies, and the most noteworthy applications of trace element studies of detrital zircon have been in “fingerprinting” potential source lithologies. The extent to which zircon trace element compositions varied globally over geological time scales (as, for example, zircon U-Pb age abundance, O isotope composition, and Hf isotope composition seem to have varied) has been little explored, and it is a topic that is well suited to the large data sets produced by detrital zircon studies. In this study we present new detrital zircon U-Pb ages and trace element compositions from a continent-scale basin system in Australia (the Centralian Superbasin) that bear directly on the Proterozoic history of Australia and which may be applicable to broader interpretations of plate-tectonic processes in other regions. U-Pb ages of detrital zircon in the Centralian Superbasin are dominated by populations of ca. 1800, 1600, 1200, and 600 Ma, and secular variations of zircon Hf isotope ratios are correlated with some trace element parameters between these major age populations. In particular, elevated εHf(i) (i.e., radiogenic “juvenile” Hf isotope composition) of detrital zircon in the Centralian Superbasin tends to correspond with relatively high values of Yb/U, Ce anomaly, and Lu/Nd (i.e., depletion of light rare earth elements). These correlations seem to be fundamentally governed by three related factors: elemental compatibility in the continental crust versus mantle, the thickness of continental crust, and the contributions of sediment to magmas. Similar trace element versus εHf(i) patterns among a global zircon data set suggest broad applicability. One particularly intriguing aspect of the global zircon data set is a late Neoproterozoic to Cambrian period during which both zircon εHf(i) and Yb/U reached minima, marking an era of anomalous zircon geochemistry that was related to significant contributions from old continental crust.

Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie einfachere Modelle?

2004 ◽  
Vol 224 (6) ◽  
Author(s):  
Christian Schumacher ◽  
Christian Dreger
Keyword(s):  
Large Scale ◽  
Factor Model ◽  
German Economy ◽  
Forecast Accuracy ◽  
Principal Component ◽  
Large Data ◽  
Factor Models ◽  
Scale Factor ◽  
Prediction Errors ◽  
Data Set

SummaryThis paper discusses a large-scale factor model for the German economy, Following the recent literature, a data set of 121 time series is used to determine the factors by principal component analysis. The factors enter a linear dynamic model for German GDP. To evaluate its empirical properties, the model is compared with alternative univariate and multivariate models. These simpler models are based on regression techniques and considerably smaller data sets. Empirical forecast tests show that the large-scale factor model almost always encompasses its rivals. Moreover, out-of-sample forecasts of the large-scale factor model have smaller prediction errors than the forecasts of the alternative models. However, these advantages are not statistically significant, as a test for equal forecast accuracy shows. Therefore, the efficiency gains of using a large data set with this kind of factor models seem to be limited.

scholarly journals Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

2020 ◽  
Vol 12 (2) ◽  
pp. 305 ◽  
Author(s):  
Tom Akkermans ◽  
Nicolas Clerbaux
Keyword(s):  
High Resolution ◽  
Global Climate ◽  
Regional Scale ◽  
Radiant Energy ◽  
Large Data ◽  
Energy System ◽  
Climate Data ◽  
Data Set ◽  
Regional Analyses ◽  
Very High

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

scholarly journals Reservoir regulation affects droughts and floods at local and regional scales

2021 ◽  
Author(s):  
Manuela Irene Brunner
Keyword(s):  
Flood Hazard ◽  
Regional Scale ◽  
Large Data ◽  
The United States ◽  
Climate Impact ◽  
Data Set ◽  
Drought And Flood ◽  
Hydrological Extremes ◽  
Reservoir Regulation ◽  
Droughts And Floods

Abstract Hydrological extremes can be particularly impactful in catchments with high human presence where they are modulated by human intervention such as reservoir regulation. Still, we know little about how reservoir operation affects droughts and floods, particularly at a regional scale. Here, we present a large data set of natural and regulated catchment pairs in the United States and assess how reservoir regulation affects local and regional drought and flood characteristics. Our results show that (1) reservoir regulation affects drought and flood hazard at a local scale by reducing severity (i.e. intensity/magnitude and deficit/volume) but increasing duration; (2) regulation affects regional hazard by reducing spatial flood connectedness (i.e. number of catchments a catchment co-experiences flood events with) in winter and by increasing spatial drought connectedness in summer; (3) the local alleviation effect is only weakly affected by reservoir purpose for both droughts and floods. We conclude that both local and regional flood and drought characteristics are substantially modulated by reservoir regulation, an aspect that should neither be neglected in hazard nor climate impact assessments.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Coupling of regional geophysics and local soil-structure models in the EQSIM fault-to-structure earthquake simulation framework

2021 ◽  
pp. 109434202110191
Author(s):  
David McCallen ◽  
Houjun Tang ◽  
Suiwen Wu ◽  
Eric Eckert ◽  
Junfei Huang ◽  
...  
Keyword(s):  
Soil Structure ◽  
Ground Motions ◽  
Regional Scale ◽  
Historical Earthquakes ◽  
Department Of Energy ◽  
Simulation Framework ◽  
Data Set ◽  
Major Barrier ◽  
Local Soil ◽  
Computational Workflow

Accurate understanding and quantification of the risk to critical infrastructure posed by future large earthquakes continues to be a very challenging problem. Earthquake phenomena are quite complex and traditional approaches to predicting ground motions for future earthquake events have historically been empirically based whereby measured ground motion data from historical earthquakes are homogenized into a common data set and the ground motions for future postulated earthquakes are probabilistically derived based on the historical observations. This procedure has recognized significant limitations, principally due to the fact that earthquake ground motions tend to be dictated by the particular earthquake fault rupture and geologic conditions at a given site and are thus very site-specific. Historical earthquakes recorded at different locations are often only marginally representative. There has been strong and increasing interest in utilizing large-scale, physics-based regional simulations to advance the ability to accurately predict ground motions and associated infrastructure response. However, the computational requirements for simulations at frequencies of engineering interest have proven a major barrier to employing regional scale simulations. In a U.S. Department of Energy Exascale Computing Initiative project, the EQSIM application development is underway to create a framework for fault-to-structure simulations. This framework is being prepared to exploit emerging exascale platforms in order to overcome computational limitations. This article presents the essential methodology and computational workflow employed in EQSIM to couple regional-scale geophysics models with local soil-structure models to achieve a fully integrated, complete fault-to-structure simulation framework. The computational workflow, accuracy and performance of the coupling methodology are illustrated through example fault-to-structure simulations.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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