Characterization of groundwater potential zones in water-scarce hardrock regions using data driven model

2021 ◽  
Vol 80 (24) ◽  
Author(s):  
Dipankar Ruidas ◽  
Subodh Chandra Pal ◽  
Abu Reza Md. Towfiqul Islam ◽  
Asish Saha
Keyword(s):  
Groundwater Potential ◽  
Data Driven ◽  
Groundwater Potential Zones ◽  
Using Data ◽  
Water Scarce

Integrated studies for characterization of lineaments used to locate groundwater potential zones in a hard rock region of Karnataka, India

2005 ◽  
Vol 14 (5) ◽  
pp. 767-776 ◽  
Author(s):  
Subash Chandra ◽  
V. A. Rao ◽  
N. S. Krishnamurthy ◽  
S. Dutta ◽  
Shakeel Ahmed
Keyword(s):  
Hard Rock ◽  
Groundwater Potential ◽  
Groundwater Potential Zones ◽  
Integrated Studies

Characterization of C7+ fraction properties of crude oils and gas-condensates using data driven models

2019 ◽  
Vol 37 (13) ◽  
pp. 1516-1522 ◽  
Author(s):  
Arash Kamari ◽  
Amir H. Mohammadi ◽  
Deresh Ramjugernath
Keyword(s):  
Data Driven ◽  
Crude Oils ◽  
Gas Condensates ◽  
Using Data

Testing foetal–maternal heart rate synchronization via model-based analyses

2009 ◽  
Vol 367 (1892) ◽  
pp. 1407-1421 ◽  
Author(s):  
Maik Riedl ◽  
Peter van Leeuwen ◽  
Alexander Suhrbier ◽  
Hagen Malberg ◽  
Dietrich Grönemeyer ◽  
...  
Keyword(s):  
Heart Rate ◽  
Data Driven ◽  
Maternal Heart Rate ◽  
Model Based ◽  
Neural Integration ◽  
Modelling Analysis ◽  
External Conditions ◽  
Using Data ◽  
Maternal Interaction

The investigation of foetal reaction to internal and external conditions and stimuli is an important tool in the characterization of the developing neural integration of the foetus. An interesting example of this is the study of the interrelationship between the foetal and the maternal heart rate. Recent studies have shown a certain likelihood of occasional heart rate synchronization between mother and foetus. In the case of respiratory-induced heart rate changes, the comparison with maternal surrogates suggests that the evidence for detected synchronization is largely statistical and does not result from physiological interaction. Rather, they simply reflect a stochastic, temporary stability of two independent oscillators with time-variant frequencies. We reanalysed three datasets from that study for a more local consideration. Epochs of assumed synchronization associated with short-term regulation of the foetal heart rate were selected and compared with synchronization resulting from white noise instead of the foetal signal. Using data-driven modelling analysis, it was possible to identify the consistent influence of the heartbeat duration of maternal beats preceding the foetal beats during epochs of synchronization. These maternal beats occurred approximately one maternal respiratory cycle prior to the affected foetal beat. A similar effect could not be found in the epochs without synchronization. Simulations based on the fitted models led to a higher likelihood of synchronization in the data segments with assumed foetal–maternal interaction than in the segment without such assumed interaction. We conclude that the data-driven model-based analysis can be a useful tool for the identification of synchronization.

Prediction and Characterization of Flooding in Pulsed Sieve Plate Extraction Columns Using Data-Driven Models

2020 ◽  
Vol 59 (44) ◽  
pp. 19726-19735
Author(s):  
Johannes Brockkötter ◽  
Michael Cielanga ◽  
Benedikt Weber ◽  
Andreas Jupke
Keyword(s):  
Data Driven ◽  
Sieve Plate ◽  
Using Data

scholarly journals Integrated studies for characterization of lineaments used to locate groundwater potential zones in a hard rock region of Karnataka, India

2006 ◽  
Vol 14 (6) ◽  
pp. 1042-1051 ◽  
Author(s):  
Subash Chandra ◽  
V. A. Rao ◽  
N. S. Krishnamurthy ◽  
S. Dutta ◽  
Shakeel Ahmed
Keyword(s):  
Hard Rock ◽  
Groundwater Potential ◽  
Groundwater Potential Zones ◽  
Integrated Studies

Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

2016 ◽  
pp. 1-21 ◽  
Author(s):  
Samira Ghorbani Nejad ◽  
Fatemeh Falah ◽  
Mania Daneshfar ◽  
Ali Haghizadeh ◽  
Omid Rahmati
Keyword(s):  
Remote Sensing ◽  
Groundwater Potential ◽  
Remote Sensing And Gis ◽  
Data Driven ◽  
Groundwater Potential Zones
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