scholarly journals Groundwater Potential Mapping in Relation to Performance of Boreholes in Weathered/Fractured Basement at Afe Babalola University Ado Ekiti Nigeria

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
S.O Oyegoke ◽  
A.S Adebanjo ◽  
O.O Ayeni ◽  
K.O Olowe
Keyword(s):  
Groundwater Potential ◽  
Yield Potential ◽  
Performance Rating ◽  
Groundwater Exploitation ◽  
Groundwater Yield ◽  
Potential Map ◽  
Potential Mapping ◽  
Groundwater Potential Map ◽  
Selection For ◽  
Groundwater Potential Mapping

This research was carried out with the aim to check the validity and efficiency of the existing groundwater potential map of Afe Babalola University, Ado Ekiti (ABUAD). The performance rating of the drilled boreholes that spread across the institution was used to validate the groundwater potential map produced by Ademilua and Eluwole in 2013. Forty (40) boreholes that were drilled in the institution were evaluated and overlaid on the existing groundwater potential map with their coordinates serially numbered. From the results obtained, it was observed that in locations designated as having good to moderate groundwater yield (potential) on the map, 21 of drilled boreholes were active, 11 were failed/dry boreholes, meanwhile, for the locations designated as having poor groundwater yield, four of drilled boreholes were active while another four were failed/dry boreholes. This result gives a 62.5% performance rating that the existing groundwater potential map can serve as a useful guide for the purpose of site selection for groundwater exploitation but for optimal usage, an improved map is required.

scholarly journals Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)

2020 ◽  
Vol 12 (3) ◽  
pp. 490 ◽  
Author(s):  
Alireza Arabameri ◽  
Saro Lee ◽  
John P. Tiefenbacher ◽  
Phuong Thao Thi Ngo
Keyword(s):  
Groundwater Resources ◽  
Groundwater Potential ◽  
Rate Curve ◽  
Yield Potential ◽  
Validation Dataset ◽  
Well Yield ◽  
Potential Map ◽  
Potential Mapping ◽  
Groundwater Potential Map ◽  
Groundwater Potential Mapping

The aim of this research is to introduce a novel ensemble approach using Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR), and random forest (RF) models for groundwater-potential mapping (GWPM) in Bastam watershed, Iran. This region suffers from freshwater shortages and the identification of new groundwater sites is a critical need. Remote sensing and geographic information system (GIS) were used to reduce time and financial costs of rapid assessment of groundwater resources. Seventeen physiographical, hydrological, and geological groundwater conditioning factors (GWCFs) were derived from a spatial geo-database. Groundwater data were gathered in field surveys and well-yield data were acquired from the Iranian Department of Water Resources Management for 89 locations with high yield potential values ≥ 11 m3 h−1. These data were mapped in a GIS. From these locations, 62 (70%) were randomly selected to be used for model training, and the remaining 27 (30%) were used for validation of the model. The relative weights of the GWCFs were determined with an RF model. For GWPM, 220 randomly selected points in the study area and their final weights were determined with the VIKOR model. A groundwater potential map was created by interpolating the values at these points using Kriging in GIS. Finally, the area under receiver operating characteristic (AUROC) curve was plotted for the groundwater potential map. The success rate curve (SRC) was computed for the training dataset, and the prediction rate curve (PRC) was calculated for the validation dataset. Results of RF analysis show that land use and land cover, lithology, and elevation are the most significant determinants of groundwater occurrence. The validation results show that the ensemble model had excellent prediction performance (PRC = 0.934) and goodness-of-fit (SRC = 0.925) and reasonably high classification accuracy. The results of this study could aid management of groundwater resources and assist planners and decision makers in groundwater-investment planning to achieve sustainability.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

scholarly journals GIS-Based Groundwater Potential Assessment in Varied Topographic Areas of Mianyang City, Southwestern China, Using AHP

2021 ◽  
Vol 13 (22) ◽  
pp. 4684
Author(s):  
Qing Zhang ◽  
Shuangxi Zhang ◽  
Yu Zhang ◽  
Mengkui Li ◽  
Yu Wei ◽  
...  
Keyword(s):  
Water Supply ◽  
Groundwater Potential ◽  
Southwestern China ◽  
Borehole Data ◽  
Hydrological Data ◽  
Potential Map ◽  
Groundwater Potential Map ◽  
Emergency Water Supply ◽  
Post Disaster ◽  
Geological Background

Mianyang City is located in the varied topographic areas of Sichuan Province in southwestern China and is characterized by a complex geological background. This area is prone to disasters and its varied topography is inconvenient for emergency water storage and supply. Groundwater is essential for alleviating the demand for water and post-disaster emergency water supply in this area. This study applied AHP to integrate remote sensing, geological and hydrological data into GIS for the assessment of groundwater potential, providing a plan for the rational exploitation of groundwater and post-disaster emergency water supply in the area. Nine factors, including the spring calibration related to groundwater, were integrated by AHP after multicollinear checks. As a result, the geology-controlled groundwater potential map was classified into five levels with equal intervals. All the results were validated using borehole data, indicating the following: the areas with yield rates of , 1–20 , and 20–400 accounted for 2.66%, 36.1%, and 39.62%, respectively, whereas the areas with yield rates of 400–4000 and accounted for only 20.88% and 0.75% of the overall area. The flexibility of this quick and efficient method enables its application in other regions with a similar geological background.

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