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In general, the hydraulic characteristics of the aquifer system are good in the middle and western side of the study area and become poor in the eastern parts. The methodology presented in the literature for modeling aquifer productivity consists of four steps: 1 describing and partitioning the borehole yield data into two sets, training and validation. A flow chart for clarifying this procedure is presented in Fig.

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To prepare thematic layers of the topographic factors, i. Elevation raster was directly created from DEM and was classified into four classes. Slope is a rise or fall of land surface. It is an important factor for groundwater potential mapping studies, because it controls accumulation of water in an area and hence enhances the groundwater recharge. The slope angle map of study area was prepared from DEM and classified into 4 classes, Fig. It is widely recognized that geology influences the occurrence of groundwater because lithological and structural variations often lead to difference in the strength and permeability of rocks and soils Ozdemir a.

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The thematic raster layer of geology was prepared by converting vector layer of geology to raster layer in ArcGIS The same converting procedure was made for HSG soil layer vector. The transmissivity and storativity are very important factors for modeling groundwater productivity because they control the ability of a specific water bearing layer to transmit and store water.

The transmissivity and storativity of the aquifer system in the study area were classified into four classes for both factors, respectively. Maps of distance from faults and river were prepared by applying the distance command in spatial analyst extension of ArcGIS Frequency ratio model The frequency ratio FR is the ratio of the probability of an occurrence to the probability of a non-occurrence for given attributes Bonham-Carter The method explores the statistical correlation between boreholes locations and the influencing groundwater occurrence factors.

In practical applications, the FR can be calculated as Ozdemir b. In information theory, entropy is a measure of uncertainty in a random variable Ihara The entropy indicates the extent of the instability, disorder, imbalance, and uncertainty of a system Yufeng and Fengxiang Shannon entropy is the average unpredictability in a random variable, which is equivalent to its information content. The entropy of groundwater reservoir yield refers to the extent that the various controlling groundwater occurrences influence the groundwater productivity.

Several influencing factors give extra entropy into the index system. Therefore, the entropy value can be used to calculate objective weights of the index system Jaafari et al. The FR ratio for the other classes was low indicating low probability of groundwater productivity.

In the literature, it is accepted that groundwater occurrence decreases as the elevation increases. It is accepted that as the slope increases, then the runoff increases as well leading to less infiltration Jaiswal et al. With respect to the study results, the FR decreases as the slope increases, but with the third slope range 6.

To interpret this, it is important to relate this range with other used factors such geology. The aerial extension of this range is mainly associated with the extension of flood deposits. These deposits consist mainly of sand and gravel and having higher values of hydraulic conductivity. The higher values of FR for flood deposits 1. In case of geology, the Quaternary lithological layers have relatively higher values of FR 1. The FRs for the rest of the lithological layers were zero indicating the low probability of groundwater occurrence. If we consider the relationship between groundwater potential and soil factor, it can be seen that FRs are high for the A and B soil groups and low for other groups.

The higher infiltration rates of these groups support the resultant higher FR values. As the infiltration rate increases the groundwater recharge increases as well leading to more productivity conditions. In the case of transmissivity and storativity factors, the FR values increase as hydraulic characteristics increase indicating high aquifer productivity conditions in the higher values of these factors.

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For distance to river factor, the highest FR values of 3. For distance to faults, the highest values of FRs occur on the first fifth classes. This implies the importance of the structural setting on the groundwater occurrence in the study area. The final groundwater productivity index for the study area was calculated using the Eq.

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The obtained GWPI was classified based on natural break classification scheme into very low, low, moderate, high, and very high classes. The weights for these factors were 0. On the other hand, the other factors distance to river, transmissivity, distance to faults, and storativity had a minor effect on groundwater productivity. The calculated weights for these factors were 0. The obtained GWPI was also classified into five classes based on natural break classification scheme, Fig. Any predictive model deterministic or stochastic requires validation before it can be used in prediction purposes.

Without validation, the model will have no scientific significant Chung and Fabbri In this context, the Receive Operating Characteristic ROC curve is usually used for examining the quality of deterministic and probabilistic detection and forecast system Swets In the ROC curve, the sensitivity of the model the percentage of boreholes pixels correctly predicted by the model is plotted against 1-specificity the percentage of predicted boreholes pixel over the total. The success rate is important to explain how well the resulting GWPI map classified the area of existing borehole locations.

The success rate results were obtained by comparing the training borehole locations 47 with the two GWPI maps. On the other hand, the prediction rate used a measure of performance of a predictive rule Yesilnacar and Topal ; Pradhan et al. It only used the testing data set to explore the predictive capability of the model. The AUC for prediction rate is shown in Figs.


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It can be seen that both models were capable to prospect GWPI with very good results, but FR was better in terms of success and prediction rates. Acknowledgments The author sincerely acknowledges the efforts of Mr. Environ Earth Sci. Pergamon Press, Oxford Google Scholar.

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Environ Earth Sci, China. Environ Earth Sci — Corsini A, Cervi F, Ronchetti F Weight of evidence and artificial neural networks for potential groundwater mapping: an application to the Mt. Modino area Northern Apennines, Italy. Geomorphology — Dar AI, Sankar K, Dar MA Remote sensing technology and geographic information system modeling: an integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin.

J Hydrol — Hydrogeo J — Arab J Geosci. Gumma MK, Pavelic P Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling. Environ Monit Assess — Ihara S Information theory for continuous systems. Int J Environ Sci Technol — Jenks GF The data model concept in statistical mapping. Int Yearb Cartogr — Google Scholar. Hydrogeol J — Arab J Geosci — Krishnamurthy J, Venkatesa K, Jayaraman V, Manuvel M An approach to demarcate groundwater potential zones through remote sensing and a geographical information system.

J Environ Manage — Hydrogeol J — Google Scholar. Malczewski J GIS and multicriteria decision analysis. Wiley, New York Google Scholar. Earth Sci Inform. Ozdemir A b GIS-based groundwater spring potential mapping in the Sultan Mountains Konya, Turkey using frequency ratio, weights of evidence and logistic regression methods and their comparison.

Appl Water Sci — Parsons RM Groundwater resources of Iraq.

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Khanaqin-Jassan area, vol 1. Photogramm Fernerkun — Satty TL The analytic hierarchy process. Geocarto Int — Spat Hydrol J —24 Google Scholar. Swets JA Measuring the accuracy of diagnostic systems. Science — CrossRef Google Scholar. Wiley, New York, p Google Scholar. Yesilnacar EK The application of computational intelligence to landslide susceptibility mapping in Turkey. PhD Thesis. University of Melbourne, Australia, p Google Scholar.


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  5. Yesilnacar E, Topal T Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region Turkey. Purchase Instant Access. View Preview. Learn more Check out. Abstract Books reviewed in this issue. Aqrawi et al. Citing Literature. Volume 33 , Issue 4 October Pages Related Information. Close Figure Viewer. Browse All Figures Return to Figure. Previous Figure Next Figure. Email or Customer ID. Forgot password? Old Password. New Password.

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