CAVE ENTRANCE LOCATION MODEL USING BINARY LOGISTIC REGRESSION: THE CASE STUDY OF SOUTH GOMBONG KARST REGION, INDONESIA

Abstract

Cave entrance data is crucial as the primary indicator in the underground river inventory of karst area. The data collection was traditionally conducted by field survey, but it is very costly and not efficient. Remote sensing and Geographic Information System (GIS) can help estimate cave entrance locations more efficiently. This study aims to 1) determine variables to identify cave entrances using remote sensing and GIS approach, and 2) examine the accuracy of the cave entrance location model. Several remote sensing data and geological data were used including ALOS PALSAR Digital Elevation Model (DEM), DEMNAS DEM, topographic map, and geological map. Topographic elements were extracted by using toposhape and Topographic Position Index (TPI). Contour derived from topographic map showed the highest accuracy to extract topographic elements compared to ALOS PALSAR DEM and DEMNAS, hence it was used for further analysis. A binary logistic regression was applied to estimate the probability of cave entrances based on the variables used. The result shows that three topographic variables, i.e., ravine, stream, and midslope drainage, had a significant value for estimating cave entrance location. Using these three variables, a logit equation was formulated to generate a probability map. The result shows that cave entrances are likely to be located in a dry valley. The accuracy assessment using the field data showed that 52.78% of cave entrances are located at medium to high potential areas. This suggests that the moderate-high potential area can indicate potential water resources in the karst area.

Keywords

• cave entrance
• karst
• GIS
• binary logistic regression
• South Gombong