Groundwater productivity potential mapping using evidential belief function.

Abstract

The evidential belief function (EBF) model was applied and validated for analysis of groundwater-productivity potential (GPP) in Boryeong and Pohang cities, agriculture region in Korea using geographic information systems (GIS). Data about related factors, including topography, lineament, geology, forest, soil, and groundwater data were collected and input into a spatial database. Additionally, in the Boryeong area, specific capacity (SPC) data not lower than 4.55 m3 /d/m were collected, corresponding to 300 m3 /d yield from 72 well locations. In the Pohang area, SPC data of ≥ 6.25 m3 /d/m were collected, corresponding to a yield of 500 m3 /d from 44 well locations. By using the constructed spatial database, 19 factors related to groundwater productivity were extracted. The relationships between the well locations and the factors were identified and quantified by using the EBF model. Four relationships were calculated: belief (Bel), disbelief (Dis), uncertainty (Unc), and plausibility (Pls). The relationships were used as factor ratings in the overlay analysis to create GPP indices and maps. The resulting GPP maps showed 83.41% and 77.53% accuracy in Boryeong and Pohang areas, respectively. The EBF model was found to be more effective in terms of prediction accuracy.