scholarly journals A non-Bayesian nonparametric model for characterization of basin-scale aquifers using groundwater level fluctuations

2021 ◽  
pp. 126710
Author(s):  
Eungyu Park ◽  
Jonghyun Lee
Keyword(s):  
Groundwater Level ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Groundwater Level Fluctuations ◽  
Basin Scale

Application of nonparametric regression to groundwater level prediction

1991 ◽  
Vol 18 (4) ◽  
pp. 600-606 ◽  
Author(s):  
Kaz Adamowski ◽  
W. Feluch
Keyword(s):  
Nonparametric Regression ◽  
Groundwater Level ◽  
Cross Validation ◽  
Accurate Prediction ◽  
Nonparametric Model ◽  
Nonparametric Regression Model ◽  
Groundwater Level Fluctuations ◽  
The Relationship ◽  
Sample Experiment ◽  
Validation Stage

A new nonparametric regression model is proposed to investigate the relationship between groundwater level fluctuations and streamflow time series observations. The developed nonparametric model does not force the relationship between variables into a rigidly defined class (i.e., linear regression) and is capable of inferring complicated relationships. The results from the analysis indicate that the nonparametric method gives more accurate prediction results than those obtained from parametric regression. A split-sample experiment shows that nonparametric regression gives accurate prediction (extrapolation) results at the validation stage. Key words: nonparametric regression, cross-validation method, groundwater level, streamflow.

scholarly journals A Bayesian nonparametric model for textural pattern heterogeneity

2021 ◽  
Vol 70 (2) ◽  
pp. 459-480
Author(s):  
Xiao Li ◽  
Michele Guindani ◽  
Chaan S. Ng ◽  
Brian P. Hobbs
Keyword(s):  
Nonparametric Model ◽  
Bayesian Nonparametric

scholarly journals The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110283
Author(s):  
Meltem Yurtcu ◽  
Hülya Kelecioglu ◽  
Edward L Boone
Keyword(s):  
Classical Test Theory ◽  
Small Sample ◽  
Test Theory ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Test Equating ◽  
Classical Test ◽  
A Value ◽  
Item Functioning ◽  
The Individual

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

scholarly journals Bayesian Nonparametric Model for the Validation of Peptide Identification in Shotgun Proteomics

2008 ◽  
Vol 8 (3) ◽  
pp. 547-557 ◽  
Author(s):  
Jiyang Zhang ◽  
Jie Ma ◽  
Lei Dou ◽  
Songfeng Wu ◽  
Xiaohong Qian ◽  
...  
Keyword(s):  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Nonparametric Model ◽  
Bayesian Nonparametric

scholarly journals Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

2018 ◽  
Vol 11 (3) ◽  
pp. 52 ◽  
Author(s):  
Mark Jensen ◽  
John Maheu
Keyword(s):  
Dirichlet Process ◽  
Joint Distribution ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Excess Returns ◽  
Dirichlet Process Prior ◽  
Stock Market Returns ◽  
Conditional Mean ◽  
Risk Return ◽  
Volatility Feedback

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution.

scholarly journals Investigation of Sentinel-1 Derived Land Subsidence Using Wavelet Tools And Triple Exponential Smoothing Algorithm In Lagos, Nigeria

2021 ◽  
Author(s):  
Femi Emmanuel Ikuemonisan ◽  
Vitalis Chidi Ozebo ◽  
Olawale Babatunde Olatinsu
Keyword(s):  
Soil Moisture ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Exponential Smoothing ◽  
Future Trend ◽  
Smoothing Algorithm ◽  
Groundwater Abstraction ◽  
Groundwater Level Fluctuations ◽  
History Of ◽  
Sbas Insar

Abstract Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate private borehole and water well proliferation. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series is computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between –94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm which is approximately 23% increase.

scholarly journals STUDY OF THE PROGRESS OF SEAWATER INTRUSION WITHIN A PLAIN AREA OF RHODOPE PREFECTURE

2004 ◽  
Vol 36 (4) ◽  
pp. 2057 ◽  
Author(s):  
Φ. Πλιάκας ◽  
I. Διαμαντής ◽  
A. Καλλιώρας ◽  
Χ. Πεταλάς
Keyword(s):  
Electrical Conductivity ◽  
Grain Size ◽  
Seawater Intrusion ◽  
Groundwater Level ◽  
Specific Electrical Conductivity ◽  
Chemical Analyses ◽  
Conductivity Measurements ◽  
Groundwater Level Fluctuations ◽  
Plain Area ◽  
Groundwater Samples

This paper investigates the progress of seawater intrusion within the plain area of Xylagani - Imeros, in SW part of Rhodope Prefecture, as well as the suitability of groundwater for several purposes, after qualitative valuation of groundwater samples from selective wells of the study area. The conclusions also include some managerial suggestions for the confrontation of seawater intrusion. The investigation in question took place between 1994-1997 and 2002-2003, and involves the installation of piezometric wells, geoelectric sounding measurements, grain size analyses, monitoring of the groundwater level fluctuations in selective wells, specific electrical conductivity measurements and chemical analyses of water samples from selective wells of the study area.

scholarly journals Analysis of long-term observations of the groundwater level In an aseismic region

2019 ◽  
pp. 47-67
Author(s):  
A. A. Lyubushin ◽  
O. S. Kazantseva ◽  
A. B. Manukin
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Atmospheric Pressure ◽  
Active Regions ◽  
Sampling Interval ◽  
Pressure Effects ◽  
Adaptive Compensation ◽  
Groundwater Level Fluctuations ◽  
And Cluster Analysis

The results of the analysis of continuous precise time series of atmospheric pressure and groundwater level fluctuations in a well drilled to a depth of 400 m in the territory of Moscow are presented. The observations are remarkable in terms of their duration of more than 22 years (from February 2, 1993 to April 4, 2015) and by the sampling interval of 10 min. These long observations are suitable for exploring the stationarity of the properties of hydrogeological time series in a seismically quiet region, which is important from the methodological standpoint for interpreting the similar observations in seismically active regions aimed at earthquake prediction. Factor and cluster analysis applied to the sequence of multivariate vectors ofthe statistical properties of groundwater level time series in the successive 10-day windows after adaptive compensation for atmospheric pressure effects distinguish five different statistically significant states of the time series with the transitions between them. An attempt to geophysically interpret the revealed states is made. Two significant periods – 46 and 275 days – are established by spectral analysis of the sequence of the transitions times between the clusters.

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