Enhancing landslide susceptibility assessment based on field-based observation along the Taba Penanjung-Kepahiang road, Bengkulu Province, Indonesia
Description
This dataset is used to perform analysis of various deep learning methods and techniques in forecasting to improve understanding of landslide susceptibility assessment based on field-based observation along the Taba Penanjung-Kepahiang road, Bengkulu Province.
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This study was conducted along the TPK road (Fig. 1), targeting nine slope sampling locations where landslide debris was observed. At each location, slope measurements were performed using a Suunto Tandem Compass Clinometer (Fig. 2b), with measurements taken between two field technicians positioned perpendicular to the slope contour following procedure proposed by (Neto and Carr, (2021). The slope height was calculated by determining the difference between the readings at the slope peak and slope tip (Karaman, 2019). Geographic coordinates at each measurement point were recorded using a handheld Global Positioning System (GPS) and compass. These points were subsequently converted into shapefiles using Quantum Geographic Information System (QGIS) and correlated with a DEM derived from the national DEM (DEMNAS) with a 5 m resolution. The resulting DEM data provided a height map, which was then correlated with the clinometer measurements and additional factors such as rainfall and previous landslide occurrences. The spatial analysis incorporated rainfall intensity, duration, and soil conditions. Rainfall data for the study area, covering the years 2017-2023, were obtained from the Google Earth Engine using the Climate Hazards Center InfraRed Precipitation with Station Data (CHIRPS) daily dataset: (Version 2.0 Final), with a resolution of 5,566 m units mm/d. These data were imported into QGIS to determine monthly rainfall levels. ). To validate the field measurements and align them with previous research that produced the LSM, the microtremor surveys were conducted at four of the clinometer sampling points. The microtremor data were collected using a three components 3D Geophone PASI Gemini SN-1405 Digital Portable seismometers. which recorded seismic waves for 30 minutes. Seismic data processing adhered to the Sesame 2004 guidelines using Geopsy software