首页|Studies from Gadjah Mada University Reveal New Findings on Machine Learning (Imp act of landslide on geoheritage: Opportunities through integration, geomorpholog ical classification and machine learning)
Studies from Gadjah Mada University Reveal New Findings on Machine Learning (Imp act of landslide on geoheritage: Opportunities through integration, geomorpholog ical classification and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Yogy akarta, Indonesia, by NewsRx correspondents, research stated, “Landslides are wi dely understood to cause damage to the geological features and the surrounding e nvironment. Our study focuses on the northern region of the Karangsambung-Karang bolong Geopark (KKNG), characterized by diverse lithology and multi-phase tecton ics.” The news correspondents obtained a quote from the research from Gadjah Mada Univ ersity: “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 morphogene sis, morphology, materials, and processes to understand the geomorphological con text, identifying three primary landforms: structural, pediments, and fluvial. F or landslide susceptibility mapping, we used the XGBoost algorithm with cross-va lidation 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 clay stone that change landforms and damage outcrops. Nevertheless, some landslides h ave positive impacts on the geosite, such as Exotic-blocks of phyllites, and Exo tic-blocks of pillow lava and radiolarian chert, because landslides can reveal f resher outcrops and rock structures, and the outcrop area becomes larger. Landsl ide mapping successfully identified geosites that are highly vulnerable and have adverse impacts, especially those with certain lithological characteristics. Th is research on viewing disaster as a harmful process has evolved into a more hol istic view of the disaster.”
Gadjah Mada UniversityYogyakartaIndo nesiaAsiaCyborgsEmerging TechnologiesMachine Learning
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