groundwater potential
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Author(s):  
Gumilar Utamas Nugraha ◽  
Andi Agus Nur ◽  
Pulung Arya Pranantya ◽  
Rachmat Fajar Lubis ◽  
Hendra Bakti

2022 ◽  
Author(s):  
Balogun Olabode Olumide ◽  
Akintorinwa Olaoluwa James ◽  
Mogaji Kehinde Anthony

Abstract Delineation of geologic features that are capable of hosting water in economic quantity in the Basement Complex has been a major concern because they are usually localized due to restricted fractured and weathered rock. To effectively evaluate the groundwater potentiality prediction index (GPPI) accuracy of an area, solely depends on the groundwater potentiality predictors (GPPs) considered and the statistical model used in analyzing the data. Therefore, the acquired remotely sensed and geophysical depth sounding database processed using autopartial curve matching software and computer aided iteration to determine was analyzed using the conventional Analytical Hierarchy Process (AHP) model and the machine learning Gradient Boosting Tree (GBT) data driven model. Such a data driven model (GBT) is efficient in solving complex and cognitive problems in high uncertainty and complex environments. Twelve (12) groundwater potentiality predictors (GPPs) namely: Digital Elevation Model (DEM), Slope (S), Drainage Density (Dd), Land Use (Lu), Aquifer Resistivity (ρa), Aquifer Thickness (h), Overburden Thickness (b), Aquifer Hydraulic Conductivity (k), Aquifer Transmissivity (Tr), Aquifer Storativity (St), Aquifer Diffusivity (D), Aquifer Reflection Coefficient (Rc). The efficacy of GBT model was applied using the Salford Predictive Modeler 8.0 software. The data were partitioned into training and test dataset in ratio 90:10 using k-10 cross validation techniques. Their prediction importance was determined and the groundwater potentiality prediction index calculated and processed in the ArcGIS environment to produce the groundwater potential prediction index (GPPI) map of the investigated area. The investigated area was classed into three (3) zonations of low, moderate and high groundwater potential with about 56% classed within the low groundwater potential zone. Fifteen (15) water column measurement from wells was used to validate the developed model by calculating the predictive correlation accuracy (PCA) using the spearman's correlation analysis. The AHP-GPPI and GBT-GPPI model gave a correlation of (rs = 0.66; p = .007) and (rs = 0.74; p = .002) respectively. In conclusion, the model has proven that the drop in aquifer resistivity doesn't necessitate the presence of groundwater but rather several parameter should be integrated together to better understand the true nature of the aquifer.


Author(s):  
Javed Akhtar ◽  
Ahmed Sana ◽  
Syed Mohammed Tauseef ◽  
Gajendran Chellaiah ◽  
Parmeswari Kaliyaperumal ◽  
...  

2022 ◽  
Author(s):  
Kouamou Njifen Serges Raoul ◽  
Eyengue A Nyam Francoise ◽  
Fossi Donald Hermann ◽  
Bikoro Bi-Alou Marcelin ◽  
Ngouokouo Tchikangoua Anita ◽  
...  

Abstract In the Campo region, groundwater is critical for human consumption and social activity. Groundwater potential is influenced by a region's geological, geophysical, and hydrogeological factors. The major goals of this research are to determine which regions are ideal for productive groundwater drilling and to assess the source of salinity in the study area's coastal aquifers. The groundwater potential map was created using Geographic Information Systems (GIS) and the Hierarchical Analysis Process (AHP). The process of groundwater mineralization was studied using principal component analysis (PCA). Six variables were taken into account, and weights were assigned to them based on their impact on groundwater recharge. In a GIS environment, spatial integration and a combination of theme layers were conducted. Campo's groundwater potential map was divided into four zones: low 14.4% (389.6 km²), moderate 53.3% (1484.5 km²), high 28.3% (783.3 km²), and extremely high 4.1% (110.9 km²). The results of the PCA reveal a mechanism of water-rock interaction, as a result of geological alteration and a salinization process caused by the intrusion of seawater and human activity The source of salinity in groundwater is manmade (agricultural and residential activities) rather than seawater intrusion. Seawater infiltration is not greatly aided by the low lineament density found near the beaches.


2022 ◽  
Author(s):  
Bilel Abdelkarim ◽  
Faten Telahigue ◽  
Belgacem Agoubi

Abstract In Gabès region (southeastern Tunisia), given the semi-arid to arid climate conditions, groundwater is an essential resource to supply the growth needs of the socio-economic development. To ensure sustainable development and preserve water resources, a careful estimation of the present day recharge amount and the delineation of the potential zones of rainfall precipitation are of required for an accurate evaluation of regional water balance. In this context, this study aims to a preliminary assessment of groundwater recharge in Gabes basin in regard to the delineation of the potential recharge areas of phreatic aquifers. Thus, a geological and hydrogeological collected database coupled with remote sensing techniques (RST) were used for the determination of the lateral variation of recharging zone ,Treatment by ArcGIS and Matlab softwares and Kohonen self-organizing maps (K-SOM) approaches.The obtained results indicate that five recharge potential areas have been identified and classified as 27% very low, 23% low, 40% moderate recharge, 7% good and 3 % very good potential recharge located principally on southern part of the study region .This distribution is controlled principally by the geomorphological, geologic, and hydrogeologic features of the region . Reasonable management strategies based on a perennial exploitation of these low renewable resources are required to optimize the water dependent socio-economic development. The estimated groundwater potential recharge of Gabès aquifer system using K-SOM and RST is of 11.4 Mm3.y-1. This recharging rate is very low it present 7% of the total rain, thus it must be ameliorated. K-SOM and RST approach is a useful method for groundwater potential recharge mapping and is a helpful of wells establishment and groundwater sustainable management.an isotopic analyses is recommended to ameliorate the decision maker to establish the adequate strategy.


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