In this study the (geoelectric – hydrogeologic) parameters which are obtained by the
quantitative interpretation of (80) Schlumberger Vertical Electrical Sounding (VES)
points distributed in six linear profiles within the study area are used in addition to
(6) pumping test locations for the groundwater reservoir located to the south of Jabal
Sinjar (Sinjar anticline). The studied area covers about 7920Km2. The (VES) field
readings were interpreted manually by using the auxiliary point method-partial
resistivity curve matching,then the interpreted results enhanced by using computer
software specialized for the 1D- (VES) resistivity curves interpretation. The (VES)
results analyzed by using modern techniques in order to construct a new predicted
hydrogeologic maps through the application of an empirical statistical relations
between geoelectric and the Hydraulic parameters. The results of empirical relations
represent the predicted hydraulic parameters for the points where no pumping tests
achieved. The results represents the predicted hydraulic conductivity (K),
Transmissivity(Tr), Specific capacity(Sc) and Total Dissolved Solids (TDS). A
computer software used to display the results as maps to display the calculated
hydrogeologic parameters variation across the studied area. This result helps to
delineate the most productive and good quality groundwater within the study area.
The Role of (Geoelectric and Hydrogeologic) Parameters in the Evaluation of Groundwater reservoir at South of Jabal Sinjar area.
geoelectrical-Hydrogeologic relations
Groundwater reservoir evaluation.
Publication Date
Tue Feb 28 2023
Journal Name
Iraqi Journal Of Science
Benchmarking Framework for COVID-19 Classification Machine Learning Method Based on Fuzzy Decision by Opinion Score Method
COVID-19
Evaluation and benchmarking
Machine learning
multi-criteria decision making
MCDM
Fuzzy Decision by Opinion Score Method
Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing
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