Impact of landslide on geoheritage: Opportunities through integration, geomorphological classification and machine learning

Abstract

Landslides are widely understood to cause damage to the geological features and the surrounding environment. Our study focuses on the northern region of the Karangsambung-Karangbolong Geopark (KKNG), characterized by diverse lithology and multi-phase tectonics. This study aims to explore (i) landslide susceptibility assessment, (ii) geomorphological characteristics and distribution of landslide susceptibility, and (iii) identification of landslide impacts on geosites. We mapped morphogenesis, morphology, materials, and processes to understand the geomorphological context, identifying three primary landforms: structural, pediments, and fluvial. For landslide susceptibility mapping, we used the XGBoost algorithm with cross-validation and utilized the area under the receiver operating characteristic curve (AUROC) for model validation. The XGBoost model revealed a high susceptibility classification for 10 geosite points. Landslides have negative impacts, such as Olistoliths of coral limestones, Exotic-blocks of chert, and calcareous red claystone that change landforms and damage outcrops. Nevertheless, some landslides have positive impacts on the geosite, such as Exotic-blocks of phyllites, and Exotic-blocks of pillow lava and radiolarian chert, because landslides can reveal fresher outcrops and rock structures, and the outcrop area becomes larger. Landslide mapping has successfully identified geosites that are highly vulnerable and have adverse impacts, especially those with certain lithological characteristics. This research on viewing disaster as a harmful process has evolved into a more holistic view of the disaster. This view includes various positive aspects that involve understanding the complex interactions between geology and geomorphology towards the geosite. By understanding the relationship between geomorphologic features (morphology, material, process, morpho-arrangement) and landslide occurrence, effective management strategies can be implemented to develop geological heritage further.

Keywords

landslide / geomorphology / machine learning / opportunities / geoheritage

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