Mapping of landslide potential in Pyeongchang-gun, South Korea, using machine learning meta-based optimization algorithms

Fadhillah, Muhammad Fulki; Hakim, Wahyu Luqmanul; Panahi, Mahdi; Rezaie, Fatemeh; Lee, Chang-Wook; Lee, Saro

doi:10.1016/j.ejrs.2022.03.008

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…The tolerance value could be acquired by subtracting 1 from the VIF value. A higher VIF value than 10 with a lower tolerance than 0.1 could indicate that each variable was dependent on each other and lead to an increase in the standard error in the predictive model [36], [59]- [61].…”

Section: ) Factor Analysismentioning

confidence: 99%

Land Subsidence and Groundwater Storage Assessment Using ICOPS, GRACE, and Susceptibility Mapping in Pekalongan, Indonesia

Hakim,

Fadhillah,

Lee

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Floods in Pekalongan, Indonesia, often occur due to river water overflowing during heavy monsoon rain. Simultaneously, the northern coastal area of Pekalongan, located adjacent to the Java Sea, has been affected by coastal floods due to sea level rise. The flood conditions in this area were exacerbated by land subsidence, leading to coastal inundation. Monitoring land subsidence in Pekalongan has become essential in predicting other possible land subsidence occurrence areas and mitigating the possible hazards caused by land subsidence. The analysis of land subsidence has been much easier since the introduction of radar satellites. In this study, 124 synthetic aperture radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2022 in descending tracks were used. The data were processed through a time-series interferometry SAR (InSAR) method based on the improved combined scatterers interferometry with optimized point scatterers (ICOPS) algorithm to provide accurate measurements over large areas by improving the selection of measurement points from persistent scatterer (PS) and distributed scatterer (DS) points using a deep learning algorithm based on a convolutional neural network (CNN), and the resulting optimized measurement points were then spatially clustered using optimized hot spot analysis (OHSA) to estimate significant points statistically and define them as hot spot points. The results of time-series deformation in Pekalongan were compared with the GPS station measurements. From the comparison, a good correlation in terms of deformation patterns between time-series InSAR and GPS measurements was observed. Our study revealed that land subsidence in Pekalongan has occurred mostly in settlement areas under the young Manuscript

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Section: ) Factor Analysismentioning

confidence: 99%

Land Subsidence and Groundwater Storage Assessment Using ICOPS, GRACE, and Susceptibility Mapping in Pekalongan, Indonesia

Hakim,

Fadhillah,

Lee

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…The resident population of the city is more than 24.9million; the city is composed of nine counties and one district, including Hanbin District, Hanyin County, Shiquan County, Ningshan County, Ziyang County, Langao County, Pingli County, Zhenping County, Xunyang County, and Baihe County. The width of city is about 200 km from east to west, and the length is about 240 km from north to south; the total area is about 23529 km 2 . The elevation of the study area is 97~2897 m, the terrain is undulating, the central terrain is low, and the northern and southern terrain is high.…”

Section: Study Areamentioning

confidence: 99%

“…The prevention and control of such frequent and destructive landslides are one of the important issues of disaster prevention and mitigation, and the key step is the evaluation of landslide susceptibility. Although there are many kinds of research results, the analysis methods and evaluation criteria are quite different [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao

Zhu

et al. 2022

Geofluids

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The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

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Assessment of Landslide Susceptibility using Geospatial Techniques: A Comparative Evaluation of Machine Learning and Statistical Models

Raut,

Dutta,

Bera

et al. 2024

Geological Journal

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This study delineates landslide susceptibility zones in the Kalimpong district by integrating multi‐sensor datasets and assessing the effectiveness of statistical and machine learning models for precision mapping. The analysis utilises a comprehensive geospatial dataset, including remote sensing imagery, topographical, geological, and climatic factors. Four models were employed to generate landslide susceptibility maps (LSMs) using 16 influencing factors: two bivariate statistical models, frequency ratio (FR) and evidence belief function (EBF) and two machine learning models, random forest (RF) and support vector machine (SVM). Out of 1244 recorded landslide events, 871 events (70%) were used for training the models, and 373 events (30%) for validation. The distribution of susceptibility classes predicted by The RF and SVM models produced similar susceptibility distributions, predicting 13.30% and 14.30% of the area as highly susceptible, and 2.42% and 2.82% as very highly susceptible, respectively. In contrast, the FR model estimated 20.98% of the area as highly susceptible and 4.30% as very highly susceptible, whereas the EBF model predicted 17.42% and 5.89% for these categories, respectively. Model validation using receiver operating characteristic (ROC) curves revealed that the machine learning models (RF and SVM) had superior prediction accuracy with AUC values of 95.90% and 86.60%, respectively, compared to the statistical models (FR and EBF), which achieved AUC values of 74.30% and 76.80%. The findings indicate that Kalimpong‐I is most vulnerable, with 6.76% of its area categorised as very high susceptibility and 24.80% as high susceptibility. Conversely, the Gorubathan block exhibited the least susceptible, with 0.95% and 6.48% of its area classified as very high and high susceptibility, respectively. This research provides essential insights for decision‐makers and policy planners in landslide‐prone regions and can be instrumental in developing early warning systems, which are vital for enhancing community safety through timely evacuations and preparedness measures.

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Mapping of landslide potential in Pyeongchang-gun, South Korea, using machine learning meta-based optimization algorithms

Cited by 12 publications

References 48 publications

Land Subsidence and Groundwater Storage Assessment Using ICOPS, GRACE, and Susceptibility Mapping in Pekalongan, Indonesia

Land Subsidence and Groundwater Storage Assessment Using ICOPS, GRACE, and Susceptibility Mapping in Pekalongan, Indonesia

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Assessment of Landslide Susceptibility using Geospatial Techniques: A Comparative Evaluation of Machine Learning and Statistical Models

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