Spatial analysis of groundwater potential mapping using the geospatial technology in the Northern Ethiopia, Amhara...

Present study was conducted in the Northern Ethiopia with the aim to identify and map potential groundwater sites using the state art and science of geo-spatial technology techniques. This method used to prepare the spatial factors derived from various sources including satellite images, existing thematic maps and to develop the model. The data employed include climatic and biophysical data like rainfall, geology, soil and land use land cover. Computation of the parameters weight impact was calculated and accordingly, geology, lineament density and geomorphology were found the most determinant factors influencing groundwater potential (GWP) occurrence; whereas, land use land cover, soil and rainfall were perceived the least significant elements. The final GWP map was developed through the integration of the selected eight variables into a system of weight overlay modeling with the ArcGIS interface environment. The result GWP map was generated in to five suitability classes as very good (14%), good (16%), moderate (24%), and the rest (47%) is under poor and very poor classes respectively. Very good potential sites were geographically lied in the plateau, flat to gentle sloppy, while areas under good classes were situated on moderately sloppy of Alaje formation under undulated surface of the study area. Similarly, the moderate potential areas were found in undulated surface of Alaje and Ashengie formations. In contrast, the poor and very poor GWP areas was lied beneath Aiba and small amount in Alaje formation in the steeply slope. Generally, the spatial distribution of the groundwater occurrence and movement in the area is mainly regulated by geology, geomorphology and lineament density. Finally, the performance of the groundwater potential of the model was cross validated by area under curve of (AUC) of receiver operating characteristic (ROC) and was found 85% accurate. Furthermore studies are demanded considering high quality data supported with intensive field measurement for better accuracy and outcome.