ORCID Profile
0000-0003-2586-6858
Current Organisation
University of Kentucky
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Publisher: Elsevier BV
Date: 10-2008
Publisher: Springer Science and Business Media LLC
Date: 15-08-2007
Publisher: Elsevier BV
Date: 10-2009
Publisher: Elsevier BV
Date: 03-2009
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 04-2008
Publisher: Springer Science and Business Media LLC
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 09-09-2017
Publisher: Elsevier BV
Date: 02-2007
Publisher: Elsevier BV
Date: 08-2012
Publisher: Association of Environmental and Engineering Geologists
Date: 11-2005
DOI: 10.2113/11.4.371
Publisher: Wiley
Date: 03-03-2009
Publisher: Elsevier BV
Date: 04-2011
Publisher: Elsevier BV
Date: 10-2008
Publisher: Elsevier BV
Date: 07-2008
DOI: 10.1016/J.JCONHYD.2007.10.005
Abstract: Although arsenic (As) contamination of groundwater in the Bengal Basin has received wide attention over the past decade, comparative studies of hydrogeochemistry in geologically different sub-basins within the basin have been lacking. Groundwater s les were collected from sub-basins in the western margin (River Bhagirathi sub-basin, Nadia, India 90 s les) and eastern margin (River Meghna sub-basin Brahmanbaria, Bangladesh 35 s les) of the Bengal Basin. Groundwater in the western site (Nadia) has mostly Ca-HCO(3) water while that in the eastern site (Brahmanbaria) is much more variable consisting of at least six different facies. The two sites show differences in major and minor solute trends indicating varying pathways of hydrogeochemical evolution However, both sites have similar reducing, postoxic environments (p(e): +5 to -2) with high concentrations of dissolved organic carbon, indicating dominantly metal-reducing processes and similarity in As mobilization mechanism. The trends of various redox-sensitive solutes (e.g. As, CH(4), Fe, Mn, NO(3)(-), NH(4)(+), SO(4)(2-)) indicate overlapping redox zones, leading to partial redox equilibrium conditions where As, once liberated from source minerals, would tend to remain in solution because of the complex interplay among the electron acceptors.
Publisher: Elsevier BV
Date: 04-2011
Publisher: MDPI AG
Date: 24-11-2021
DOI: 10.3390/W13233330
Abstract: Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.
Location: United States of America
No related grants have been discovered for Alan Fryar.