Safety factor analysis of landslides hazard as a result of rain condition infiltration on Buyan-Beratan Ancient Mountain

Sinarta, I Nengah; Basoka, I Wayan Ariyana

doi:10.1088/1742-6596/1402/2/022002

Cited by 11 publications

(10 citation statements)

References 2 publications

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“…The application of ML methods in landslide susceptibility modeling first gained significant attention in the early 2000s and achieved widespread popularity among scholars a decade later [48]. The study compares several ML approaches, including Logistic Regression, Artificial Neural Networks (ANNs), Support Vector Machines (SVM), Random Forest (RF), k-nearest Neighbors (KNN) [49], Naive Bayes (NB), and Decision Trees (DT) [50]. These methods have been scrupulously tested to establish robust models capable of predicting and analyzing the susceptibility of areas to landslides effectively.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

He,

Wang,

Wang

et al. 2024

Sustainability

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The accuracy of Seismic Landslide Susceptibility Maps (SLSMs) is imperative for the prevention of seismic landslide disasters. This study enhances the precision of SLSMs by integrating nine distinct machine learning methodologies with the GeoDetector version 0.0.4 tool to filter both numerical and physical factors contributing to landslide susceptibility. The dataset comprises 2317 landslide instances triggered by the 2013 Minxian Ms = 6.6 earthquake, from which redundant factors were pruned using the Recursive Feature Elimination technique. Subsequent evaluations of the optimized factors, both individually and in combination, were conducted through Frequency Ratio analysis and Factor Interaction assessment. The study juxtaposes the Area Under the Receiver Operating Characteristic Curve (AUC) and the accuracy of nine machine learning models before and after factor optimization. The findings indicate an increase in AUC from a maximum of 0.989 to 0.992 in the Random Forest model, and an 8.37% increase in AUC for the SVM model, signifying a notable enhancement in the stability across all models. The establishment of the SLSM notably elevated the frequency ratio in high-risk zones from 50.40 to 85.14, underscoring the efficacy of combining machine learning and detector optimization techniques in sustainable practices. This research proposes a universal framework aimed at eliminating redundancy and noise in SLSMs and hazard risk assessments, thereby facilitating sustainable geological disaster risk management.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

He,

Wang,

Wang

et al. 2024

Sustainability

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“…Previous research conducted by Ragil and Mina analyzed the behavior of the piles in clay soil conditions where the safety factor obtained was 4.7 with normal soil conditions [14], [15]. Unlike the case with granular soil at the planning location of the Warmadewa Hospital building, which is designed on granular soil, which is predominantly sand, of course, it has a different safety factor value depending on the soil [16], [17] property data. Further analysis is needed to optimize the planning design results because using only the Mayerhoof method is not optimal because the study focuses only on similar soil conditions [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Safety Factor and Settlement Analysis of Borepile Foundation in Warmadewa Gianyar Hospital Building

Sinarta,

Gede Dharma Wisnawa,

Agung Gede Sumanjaya

2024

Civense

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Building structure planning must be distinct from foundation structure planning. The foundation plays an important role in transferring building loads to the ground. The Warmadewa Gianyar Hospital building consists of 5 floors with a building area of 8323. 471 m2 on a granular soil location where there are three layers of soil types, namely sandy silt soil, silty sand, and sand mixed with rock grains. The Borepile foundation is installed at a depth of 12 m with an N SPT value of 60. This building carries dead loads, live loads, rain loads, wind loads, and earthquake loads. This building will use a bore pile foundation by modeling bore pile piles in GEO5 software to obtain safety factor values and installed reinforcement. Based on the analysis results, the pile diameter used is 450 mm and has a length of 12 m. The analysis obtained the carrying capacity with a safety factor of 2.1> 2 and a maximum settlement of 4.77 mm.

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“…Landslide disaster data states that in 2020, Bangli Regency experienced 27 landslides, or 16.26% of all landslides in Bali [3]. The debris flow that occurs is dominated by high rainfall, and in villages located on Mount Batur and Mount Abang, such as Trunyan Village, Abang Batudinding Village, and Buahan Village [4], debris flow triggers due to volcanic soil conditions with loose rocks, steep slopes, lack of vegetation, geological structure, high rainfall intensity, and long rain duration [5,6].…”

Section: Introductionmentioning

confidence: 99%

Influence of Grassroots on the Stability of Slopes: Experimental Modelling and Numerical Analysis

Sinarta

2024

GEOMATE

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Debris flow disasters were caused by surface erosion with high rainfall, especially in mountain villages on the island of Bali. This research aims to understand the stability behavior of volcanic soil due to physical and mechanical conditions based on numerical analysis with Plaxis 3D software. The experiment was used by planting in a test box in the form of a steel frame and equipped with a rainfall simulator to determine slope stability due to planting a combination of elephant grass and vetiver. Numerical results for volcanic soil conditions without vegetation show a soil tension of 0.6854 kN/m 2 . Model results after four days, there was a decrease of 0.0001m, and after 33 days there was a decrease of 0.0000064 m for the slope 45°. The safety factor of slopes without vegetation is 0.8, so this condition has the threat of landslides and surface erosion. Experimental results show that adding elephant grass combined with vetiver grass can reduce erosion by 94.6% on slope 45 o and 92.67% on slope 60 o . The effectiveness of the resulting runoff reaches 55.48% on slope 45 o and 53.89% on slope 60 o . This increase in effectiveness was caused by the development of elephant grassroots, which spread horizontally, thereby increasing the bond with the top layer of soil.

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Safety factor analysis of landslides hazard as a result of rain condition infiltration on Buyan-Beratan Ancient Mountain

Cited by 11 publications

References 2 publications

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

Safety Factor and Settlement Analysis of Borepile Foundation in Warmadewa Gianyar Hospital Building

Influence of Grassroots on the Stability of Slopes: Experimental Modelling and Numerical Analysis

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