Ph.D. DISSERTATION SUBMITTED TO THE FACULTY OF WATER RESOURCES AND IRRIGATION ENGINEERING, ARBA MINCH WATER TECHNOLOGY INSTITUTE, SCHOOL OF POST GRADUATE STUDIES, ARBA MINCH UNIVERSITY

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dc.contributor.author ASNAKEW MULUALEM TEGEGNE
dc.date.accessioned 2025-10-20T13:08:42Z
dc.date.available 2025-10-20T13:08:42Z
dc.date.issued 202-10
dc.identifier.uri http://hdl.handle.net/123456789/2486
dc.description.abstract The evaluation of groundwater potential and quality is crucial for sustaining human and animal life, as well as agricultural systems. Groundwater extraction is vital for various (i.e., domestic and agricultural) purposes in semi-arid and humid regions. However, there is no sufficent comprehensive studies on quantity and quality of groundwater in the Guna-Abay watershed in Ethiopia. Therefore, this study aimed to assess recharge, groundwater potential, current groundwater quality status, and potential risks of groundwater quality deterioration in the area. WetSpass-M model was applied to evaluate spatial groundwater recharge by incorporating various data such as rainfall, wind speed, temperature, evapotranspiration, land cover, elevation, soil, slope, and groundwater depth. Geographical detectors were employed to quantify the influencing factors of recharge and their interactions. Influencing factors of groundwater potential such as recharge, drainage density, transmissivity, lithology, geomorphology, and lineament density were quantified using geo-detectors. The process of convolutional neural network (CNN) approach showed an accuracy of 86.84%, which was more reliable and acceptable. Furthermore, groundwater quality for irrigation and drinking purposes was evaluated using an entropy-weighted drinking water quality index (EWQI), indicators, and an index for irrigation water quality. About 78 groundwater samples were collected and measured (physicochemical parameters) from 39 locations during dry and wet seasons. Finally, the groundwater quality deterioration has been evaluated using groundwater contamination index (GCI) and geographical detectors were applied to identify and quantify the cause of deteriorating groundwater quality. The study results indicated five zones of the spatial groundwater recharge (in mm) distribution: very high (205.88-508.72), high (128.12 205.88), moderate (66.74–128.12), low (25.81–128.12), and very low (0–25.81). In the northwest part of the study area, the high recharge zone is dominant; however, due to soil erosion and runoff susceptibility, its contribution is low to groundwater potentiality. Soil was the most governing infuencial factors for recharge potential in the study area. The factors influencing groundwater potential were transmissivity, recharge, lineament density, lithology, drainage density, and geomorphology. Analysis of the groundwater potential index revealed five categories: very low (0-0.27), low (0.27-0.41), moderate (0.41-0.53), high (0.53-0.66), and very high (0.66-1.00). The higher transmissivity potential, the northwest part of the Guna-Abay watershed exhibited very high groundwater potential, while other areas showed low-very low potential. Reralively higher groundwater potential areas have a better quality. Groundwater quality for drinking water was classified as excellent (84.6%), good (12.8%), and medium (2.6%) based on EWQI values in both seasons. The irrigation quality index indicated that approximately 85% of samples are suitable and 15% doubtful for irrigation. This study provides valuable insights for research scholars, policymakers, groundwater experts, farmers, and environmental researchers. The findings can aid groundwater resource management and future water scarcity challenges by identifying potential water regions and quantifying key influencing factors. en_US
dc.language.iso en en_US
dc.subject Convolutional neural network, geographical detectors, groundwater contamination index, groundwater potential, groundwater quality index and recharge en_US
dc.title Ph.D. DISSERTATION SUBMITTED TO THE FACULTY OF WATER RESOURCES AND IRRIGATION ENGINEERING, ARBA MINCH WATER TECHNOLOGY INSTITUTE, SCHOOL OF POST GRADUATE STUDIES, ARBA MINCH UNIVERSITY en_US
dc.type Thesis en_US


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