GIS-Based Logistic Regression Application for Landslide Susceptibility Mapping in Son La Hydropower Reservoir Basin

Phong, Tran Van; Dam, Nguyen Duc; Trinh, Phan Trong; Dũng, Nguyễn Văn; Hiếu, Nguyễn Văn; Tran, Cuong Q.; Van, Tung Duc; Nguyen, Quan Cong; Prakash, Indra; Pham, Binh Thai

doi:10.1007/978-981-16-7160-9_186

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…LR is one of the most reliable approaches for evaluating landslide susceptibility and optimizing the model using the most significant factors [ 45 , 90 ]. The factor labeling with 1 for occurrence and 0 for non-occurrence locations is considered the categorical-binary dependent variable.…”

Section: Methodsmentioning

confidence: 99%

“…The authors pretend to answer the following questions based on the described scientific and practical issues: (i) How do LR and EFA evaluate the significance of landslide occurrence factors? LR has been widely used for landslide susceptibility mapping, as it allows identifying significant factors related to landslides [ [43] , [44] , [45] ]. However, EFA, as a multicriteria technique, has not been exploited for Landslide factor analysis as in other areas [ 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador)

Cobos-Mora,

Rodriguez-Galiano,

Lima

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador)

Cobos-Mora,

Rodriguez-Galiano,

Lima

2023

Heliyon

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“…Sistem Informasi Geografis merupakan tools yang sangat berperan penting untuk mengidentifikasi wilayah potensi tanah longsor secara spasial dan temporal di Kota Ambon (Bhunia and Shit, 2022;Phong et al, 2022). Salah satu metode SIG yang paling sederhana dan akurat untuk mengidentifikasi daerah potensi longsor adalah metode slope morphology atau SMORPH (Ramdhoni et al, 2020).…”

Section: Pendahuluanunclassified

Analisis Spasial Daerah Potensi Rawan Longsor Di Kota Ambon Dengan Menggunakan Metode Smorph

Rakuasa

Supriatna

Tambunan

et al. 2022

JTSL

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The geographical condition of Ambon City, which is 75% a hilly area resulted in most communities building in marbled areas with slopes above 20%, which has the potential to threaten life and landslide disasters. This study simply looked at the influence of slopes and slope shapes in Ambon City that can be analyzed using geographic information systems (GIS) to map areas that have the potential for landslides. Identification and mapping of potential landslide areas have an important role as an effort in overcoming and anticipating the occurrence of landslide disasters. This study aimed to analyze the spread of potential landslide areas in Ambon City based on the results of SMORPH modeling. The study used the slope morphology or SMORPH method, which has a better degree of accuracy than the Storie Index and SINMAP methods to identify and classify potential landslide areas based on the matrix between slope shape and slope angle. This study resulted in 4 levels of landslide potential areas, namely very low, low, medium and high potential. Areas with high landslide potential dominate the northern and southern parts of Ambon City. In the region, most landslides occur in the form of sunken and convex slopes. The region has a hilly and mountainous topography with a steep slope. The results of this research using the SMORPH method can illustrate that the slope of the increasingly higher slope accompanied by the shape of a convex or concave slope will cause the potential for landslides that are higher in the region.

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“…Identifying and mapping landslide-prone areas are essential in mitigating future landslide disasters (Hamida & Widyasamratri, 2019). The Geographic Information System is a tool that plays an essential role in identifying potential landslide areas spatially and temporally in Ambon City (Bhunia & Shit, 2022;Van Phong et al, 2022). One of the simplest and most accurate GIS methods for identifying potential landslide areas is the slope morphology or SMORPH method (Ramdhoni et al, 2020;Mufidawati et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Identification of Landslide Prone Areas Using Slope Morphology Method in South Leitimur District, Ambon City

Sugandhi

Supriatna²,

Rakuasa³

2023

Jambura Geosci. Rev.

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South Leitimur District is one of the districts in Ambon City where landslides often occur, and this disaster causes many losses. One of the mitigation efforts is mapping areas with the potential for landslides to determine their distribution and risks. This study aims to apply the slope morphology method to identify landslide-prone areas in South Leitimur Regency. This study uses a Digital Elevation Model (DEM) extracted into the shape of slopes and slopes and processed using ArcGIS 10.8 software. This study uses the slope morphology method or SMORPH to identify and classify areas with potential landslides based on the matrix between the slope's shape and angle. The results of the study were classified into four classes of landslide potential, namely very low potential with an area of 2,489, 53 ha, low with an area of 3,278, 22 ha, medium with an area of 672, 32 ha, and high with an area of 685, 67 ha. Hutumury Village is a village that has the largest landslide potential area in each class of landslide potential in the South Leitimur District; this is because this village is a village that has the most significant area compared to other villages. The village that has a low landslide potential is Ema Village. The results of this study also illustrate that the higher the slope with convex or concave slopes, the higher the potential for landslides. The results of this study are expected to help the government of South Leitimur Regency in efforts to mitigate landslides in the future.

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GIS-Based Logistic Regression Application for Landslide Susceptibility Mapping in Son La Hydropower Reservoir Basin

Cited by 3 publications

References 11 publications

Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador)

Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador)

Analisis Spasial Daerah Potensi Rawan Longsor Di Kota Ambon Dengan Menggunakan Metode Smorph

Identification of Landslide Prone Areas Using Slope Morphology Method in South Leitimur District, Ambon City

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