African Journal on Land Policy and Geospatial Sciences (Sep 2024)
A GIS and RS Multi-criteria Analysis of Prospective Groundwater Zones in Undulating Terrain: Wami-Ruvu Basin, Tanzania
Abstract
Context and background Groundwater is the most vital and promising natural resource for ecosystems and societies particularly in semi-arid areas. Due to urbanization and various anthropogenic activities the demand of water is hugely increasing. Proper evaluation and demarcation of groundwater potential zones can facilitate proper physical exploration of ground water and hence simplify identification of proper locations for borehole drilling. Sustainable use of groundwater is essential in Tanzania so as to increase long-term agricultural and industrial sustainability as well as to maintain the pace of socio-economic development for poverty reduction and eradication. Goal and Objectives: The objective of this research is to delineate prospective zones for groundwater exploration in Wami-Ruvu Basin Water Board (BWB), Tanzania.. Methodology: A Remote Sensing (RS) and Geographic Information System (GIS) based on Analytical Hierarchical Process (AHP) has been utilized. Results: The final groundwater potential map was prepared by assigning appropriate weightage and integration of thematic layers using weighted overlay analysis. The groundwater potential areas have been categorized into five categories very low (0.3%), low (32.0%), moderate (62.8%), high (4.8%) and very high (0.1%). Validation of the identified potential zones indicates a Pearson correlation of 0.6 between yields and GWPZ. This study clearly highlights the efficacy of RS and GIS-based multi-criteria decision technique as useful modern approach for proper groundwater resources evaluation; providing quick prospective guides for groundwater exploration and exploitation. We recommend RS and GIS based identification of Ground potential zones for all Basin Water Boards in Tanzania to support physical groundwater exploration. Future work could test the use of machine learning algorithm particularly random forest and deep learning and incorporation of other parameters for delineating groundwater potential zones.
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