scholarly journals Geodetic data inversion using a Bayesian information criterion for spatial distribution of fault slip

1992 ◽  
Vol 109 (2) ◽  
pp. 363-375 ◽  
Author(s):  
T. Yabuki ◽  
M. Matsu'ura
Keyword(s):  
Spatial Distribution ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Fault Slip ◽  
Geodetic Data ◽  
Data Inversion

scholarly journals An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 400
Author(s):  
Dmitrii Shadrin ◽  
Artyom Nikitin ◽  
Polina Tregubova ◽  
Vera Terekhova ◽  
Raghavendra Jana ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Natural Resources ◽  
Groundwater Quality ◽  
Gaussian Process ◽  
Bayesian Information Criterion ◽  
Gaussian Process Regression ◽  
Information Criterion ◽  
Machine Learning Techniques ◽  
Distribution Models ◽  
Study Region

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher R2 score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.

scholarly journals Development of a Trans-dimensional Fault Slip Inversion for Geodetic Data

2020 ◽  
Author(s):  
Fumiaki Tomita ◽  
Takeshi Iinuma ◽  
Ryoichiro Agata ◽  
Takane Hori
Keyword(s):  
Fault Slip ◽  
Geodetic Data

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

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