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Geographical Information System and Multi-Influencing Factor techniques for the assessment of groundwater potential zones of tropical watersheds: A case study
15-28Views:128The groundwater of a region is predisposed much by terrain factors along with climatic factors. Being a subsurface phenomenon groundwater cannot be observed directly, it had to be done indirectly based on the analysis of data of observable features collected through field observation and various geophysical techniques. Groundwater occurrence is a phenomenon which is heavily dependent on multiple parameters viz., Geomorphology, geology, land use, slope, soil texture, drainage density, lineament density, relief, and vegetation. The present study intends to examine the need and prospect of assessing the groundwater potential of a tropical watershed on Geographical Information System (GIS) platform using the Multi-Influencing Factor (MIF) technique. A multi-influencing factor approach is used, where weights were assigned to each factor based on their level of influence and finally conducted weighted overlay platform to generate groundwater potential zones of the Perumba watershed, in the Kannur District of Kerala, India. Various parameters of groundwater have been analyzed from a geomorphological standpoint and potential zones were identified and categorized. The study signifies the application of MIF and GIS techniques in hydrological studies, as they qualify for comprehensive assessment and spatial analysis of various factors that affect the dynamics of groundwater. The results were verified through a field survey.
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Integrated approach for lineament extraction in assessing groundwater potential: a case study in the highlands of Eritrea
1-14Views:156Groundwater exploration is crucial for sustaining ecosystems and human activities, particularly in semi-arid regions like the highlands of Eritrea. This study evaluates the effectiveness of manual and automated lineament extraction methods for identifying geological structures indicative of groundwater potential. Using a 30-meter resolution SRTM digital elevation model (DEM), we generated hillshade images and extracted lineaments manually by ESRI ArcGIS and automatically with MATLAB's edge detection algorithms, including Canny, Sobel, Prewitt, Roberts, Approximate Canny, and Laplacian of Gaussian (LoG). Our findings reveal significant differences between the manual and automated methods. Directional analysis showed that manually identified lineaments primarily followed NE-SW and NW-SE directions. Among the automated methods, Canny, Approxcanny, and Prewitt exhibited similar directional trends. Lineament density maps indicated high-density areas in the western and northern parts of the study area for both manual and automated methods. The LoG algorithm showed the highest correlation (0.8561) with the manual method, suggesting it as a reliable alternative for lineament mapping, contributing to more efficient groundwater exploration in semi-arid regions.