International Conference on Engineering.
Applied Science And Technology The Landslide Risk Level Evaluation in South Sumatra Province Reffanda Kurniawan1*.
Heni Fitriani 2.
Yulindasari Sutejo2.
Bimo Brata Adhitya2 1Ph.
D Student.
Engineering Science Doctoral Program.
Faculty of Engineering.
Universitas Sriwijaya.
Indonesia 2 Department of Civil Engineering.
Faculty of Engineering.
Universitas Sriwijaya.
Jl.
Palembang -Prabumulih KM.
32 Indralaya.
Ogan Ilir 30662.
South Sumatera.
Indonesia
ARTICLE INFO
ABSTRACT
Keywords:
Landslide Risk Level Hazard Vulnerability Capacity Landslides in South Sumatra Province often occur due to high rainfall, which causes unstable soil, often resulting in road closures and blocking residential areas.
Therefore, an evaluation of the level of natural disaster risk for landslides needs to consider the components of hazard, vulnerability, and capacity.
This is essential for producing accurate and relevant risk maps, especially for the South Sumatra Province.
The results of this research show that the level of landslide risk is influenced by hazard, vulnerability, and capacity.
The hazard variables include nine variables, namely: rainfall, geology, land use, slope, soil type, soil bearing capacity around bridges, safety factors, zone typology, and landslide history and frequency of occurrence around road sections.
The vulnerability risk level variables include fourteen variables consisting of physical, social, economic, and environmental vulnerability.
In addition, the landslide risk level variables from the capacity risk level factor consist of four variables comprising social, economic, and institutional aspects.
Through this landslide risk modelling based on hazard, vulnerability, and capacity assessments, this research is expected to provide input for more effective disaster mitigation, management, and adaptation planning.
A 2025 International Conference on Engineering.
Applied Science and Technology.
All rights reserved Introduction O Landslides are events caused by greater driving forces, namely angle, water, load, and soil/rock density, compared to the restraining forces of rock and soil density .
Landslides are one of the natural disasters that frequently occur in Indonesia, especially in areas with hilly topography and high rainfall such as South Sumatra Province.
South Sumatra Province has many areas that are highly vulnerable to landslides.
This is because landslides occur in almost every border area in South Sumatra Province during the rainy season, making it a serious problem that must be addressed.
Landslides naturally occur due to, among other things, the decline in the stability of a slope as a result of soil/rock degradation and human activity.
Corresponding author.
E-mail address: 03013622328011@student.
Based on data from the Central Statistics Agency (BPS) in 2025, between 2018 and 2023, there were 36 natural landslide disasters in South Sumatra Province.
The districts/cities that have experienced landslides include: Ogan Komering Ulu.
Muara Enim.
Lahat.
Musi Banyuasin.
Ogan Komering Ulu Selatan.
Empat Lawang.
Penukal Abab Lematang Ilir.
Pagar Alam, and Lubuk Linggau.
The district of Ogan Komering Ulu Selatan is one of the areas that has experienced the most landslides, with 12 In disaster mitigation efforts, landslide risk modelling is a very important tool.
This modelling approach is generally based on three main components .
: hazard, vulnerability and capacity.
Hazard analysis includes an assessment of the likelihood of landslides based on physical factors such as slope gradient, soil type, rainfall, and land Meanwhile, vulnerability describes the potential level of damage or impact on exposed elements, such as settlements, public facilities, and International Conference on Engineering.
Applied Science And Technology e-ISSN : 3110 - 3154 economic activities.
Capacity refers to the ability of the community and local government to cope with and reduce the impact of landslides, whether through infrastructure, preparedness, or policy.
Capacity: the capacity of local governments and communities to deal with disasters varies, with a need to improve early warning systems, training, and supporting infrastructure.
Research conducted by .
on landslide risk analysed hazard parameters .
lope, rainfall, soil typ.
, vulnerability .
ettlements, roads, population densit.
, and capacity .
vacuation facilities, warning Each parameter was weighted and analysed using spatial overlay in a Geographic Information System (GIS) using a risk assessment method in accordance with National Board for Disaster Management (BNPB) Regulation No.
2 of Areas with high risk levels are dominated by areas with steep slopes, high rainfall, and low settlement density with low mitigation capacity.
This resulted in a landslide risk zoning map classified into low, medium, and high risk.
Meanwhile, according to .
, the disaster risk assessment framework consists of four main elements, namely hazard, disaster potential, vulnerability, and .
analysed the risk of road networks to landslides by considering aspects such as: hazards .
Vulnerability .
oad function, road repairs, alternative route.
and Capacity .
one mapping, road repair.
The study was conducted in six counties located on the border between Sichuan and Yunnan provinces in China, an area prone to geological disasters.
The research by .
on the assessment of the index and level of landslide disaster risk on road sections in Sungai Penuh City and Kerinci Regency in 20202021 found 13 landslide events.
The parameters analysed were hazard, exposure, vulnerability, external context, and capacity.
This study used scoring and weighting techniques on spatial data, which were then overlaid using GIS.
The results of the study of 10 national road sections spanning 5 km showed that 7 sections were classified as low risk .
72%), mostly located within the city, and 3 sections were classified as medium risk .
28%),
generally connecting roads between cities.
The landslide hazard index is classified as low and Although most sections are in the moderate risk category, risk index-based mapping is crucial as a basis for decision-making on road infrastructure mitigation.
The South Sumatra National Disaster Management Agency (BNSP) .
analysed the risks by identifying hazard, vulnerabilities, and the capacity of the region to deal with disasters.
This approach includes an assessment of 14 types of disaster hazard grouped into geological, hydrometeorological, and anthropogenic categories.
The main findings are: .
Disaster hazards: South Sumatra faces various disaster hazards, including earthquakes, floods, landslides, forest and land fires, and disease .
Vulnerability: factors such as population density, socio-economic conditions, and infrastructure affect the region's vulnerability to Disaster risk assessment is an effort to produce information related to the level of disaster risk in an The level of risk is obtained from a combination of three components, namely Hazard.
Vulnerability, and Capacity.
The disaster risk formula is as follows:
Risk = Hazard y Vulnerability Capacity .
These three components .
azard, vulnerability, capacit.
are determined based on their respective The hazard component is determined through an analysis of probability .
ikelihood of occurrenc.
and intensity .
everity of occurrenc.
The vulnerability component is calculated based on four parameters, namely social vulnerability .
xposed populatio.
, economic vulnerability .
oss of productive lan.
, physical vulnerability .
ssues due to damage to houses and building.
, and .
nvironmental Finally, the capacity component is determined using the parameter of regional resilience .
overnment The combination of these three components results in a risk that provides information about the comparison between the vulnerability and capacity of a region in facing disasters.
In other words, the level of risk indicates the ability of a region to reduce the impact of losses caused by disasters.
Methods This research was quantitative research with an explanatory and verificative approach.
The aim is to explain the relationship between the cause and effect variables in the level of landslide risk in South Sumatra Province.
The data collection stage aims to analyses the parameters that influence and cause landslides in the South Sumatra Province and to model the level of landslide risk.
The data required The Landslide Risk Level Evaluation in South Sumatra Province Reffanda Kurniawan.
Heni Firiani .
Yulindasari Sutejo.
Bimo Brata Adhitya International Conference on Engineering.
Applied Science And Technology 2025 in this study are primary and secondary data.
The data collection flowchart is described in Figure 1.
Figure 2.
Landslide on the Pagar Alam Ae Tanjung Sakti Road at KM 320 500 Figure 1.
Research Flow Chart Primary data includes: field surveys of geomorphological conditions, vegetation, and questionnaires for road users, community leaders, and authorities.
soil tests covering texture, moisture, and slope inclination.
Secondary data includes: geological maps, land use, slope inclination (GIS).
rainfall data from the BMKG.
landslide data from the South Sumatra Provincial BNPB from 2020 to 2024.
and RTRW and other spatial data from relevant agencies.
This research was conducted in the South Sumatra Province, focusing on the districts of South OKU.
Lahat, and Muara Enim.
Previous research containing summaries of national and international journals and national and international proceedings related to the risk of landslide vulnerability is shown in Table 2.
The results of previous research summaries are based on the level of landslide risk, which is grouped into hazards, vulnerability, and capacity.
Based on this summary, the researchers used landslide risk levels influenced by hazards, vulnerability, and capacity.
The hazard variables included nine variables.
The vulnerability risk level variables consist of physical, social, economic, and environmental vulnerabilities.
The variables for landslide risk level from the risk capacity factor consist of: .
social, .
economic, and .
Figure 3 shows the research variables used in this research.
Results and Discussions South Sumatra Province is located at 1A 37' 27'' Ae 4A55' 17'' South Latitude and 102A 3' 54'' Ae 106A 13'26'' East Longitude.
South Sumatra Province, with its capital in Palembang has an area of 91,179,739 kmA.
Based on its geographical position, the administrative boundaries of South Sumatra Province are as follows: North: borders Jambi Province.
South: borders Lampung Province.
West:
borders Bengkulu Province.
East: borders Bangka Belitung Province The administrative area of South Sumatra Province consists of 13 regencies, 4 cities, 241 sub districts and 3,289 villages.
Based on Regulation o f the Minister of HomemAffairs of the Republic of Indonesia Number 72 of 2019, the capital and area of each regency/city in South Sumatra Province are shown in Table 1.
One of the landslide locations in South Sumatra Province is shown in Figure 2.
International Conference on Engineering.
Applied Science And Technology e-ISSN : 3110 - 3154 Tsble 1.
Area of Regencies/Cities in South Sumatra Province No.
Regency / City Banyuasin
Empat Lawang
Lahat
Muara Enim
Musi Banyuasin
Musi Rawas Musi Rawas Utara
Ogan Ilir
Ogan Komering Ilir
Ogan Komering Ulu
OKU Selatan OKU Timur
Penukal Abab Lematang Ilir
City Lubuk Linggau / Pagar Alam
Pagar Alam
Palembang
Prabumulih
Sumatera Selatan Province Percentage of Province Area
(%)
Capital Wide .
Pangkalan Balai Tebing Tinggi Lahat Muara Enim Sekayu Muara Beliti Muara Rupit Indralaya Kayu Agung Baturaja Muara Dua Martapura Talang Ubi Lubuk Linggau Pagar Alam Palembang Prabumulih Palembang Figure 3.
Map of Landslide Locations in 2023 in South Sumatra Province ( BPPJN Report, 2.
The Landslide Risk Level Evaluation in South Sumatra Province Reffanda Kurniawan.
Heni Firiani .
Yulindasari Sutejo.
Bimo Brata Adhitya International Conference on Engineering.
Applied Science And Technology 2025 Table 2.
Summary of Previous Research Based on Risk Level Articles.
Locations Alcyntara-Ayala, 2025 (Africa.
Americas.
Asia.
Europe.
Oceani.
Capobianco et al.
, 2025 (European Regio.
Septiana, et al.
, 2024 (Sunda Strai.
Zhou et al.
, 2024 (Chin.
Diputra et al.
, 2023 (Lubuk Lingga.
Pratiwi.
, 2023 (Bogo.
Liu et al.
, 2022 (Portuga.
Prihatin et al.
, 2022 (Purworejo Regenc.
Hazard Natural factors Human activities Rainfall Climate Land use Earthquake Location Volcanic zone 4 Potential tsunami Geology Topography Slope Gradient Land clearing 3 Fault/Fracture Rainfall Slope gradient Soil type Rainfall Climate Rainfall Steep topography Soil type Rainfall Topography Panchal and Shrivastava, 2022 (Indi.
3 Land use Geology Rainfall Arambepola and Devkota, 2021 (Lao.
Geomorphological Bahri et al.
, 2021 (Ngantang Regency.
East 1.
Volcanic hazards Jav.
Landslide Sari, et al.
, 2021 (Kerinci Regency and Rainfall Sungai Penuh Cit.
Geology Frequency of landslides Model susceptibility Wang et al.
, 2021 (Fengjie County Chin.
GeoDetector 14 Wahyudianto, 2021 (East Jav.
Rainfall Historical landslides BNPB South Sumatra Report, 2021 (South 1.
Rainfall 15 Sumatr.
Geological conditions Land use Aminatun and Muntafi, 2020 Rainfall 16 (Bantul.
Yogyakart.
Slope gradient Weathered soil Madria et al.
, 2020 (Kota Kinabalu.
Steep slope 17 Sabah.
Malaysi.
Human activities 18 Nugroho, et al.
, 2020 (Kulonprogo Regenc.
19 Hamida and Wdyasamratri, 2019 City.
Bantul Regency.
Buleleng Regenc.
20 Firmansyah, et al.
, 2019 (Bukittinggi Cit.
Asteriani et al.
, 2019 (City of Siak Sri 21 Indrapur.
22 Wijaya, 2018 (Cilacap Regenc.
Gariano and Guzzetti, 2016 (Asia.
South 23 America, and Afric.
Saputra and Ardhana.
, 2016 (Buleleng Regenc.
25 Faizana, et al.
, 2015 (Semarang Cit.
Vulnerability Socio-economic aspects Urbanization Public awareness Population Infrastructure Topographic conditions Geographical location Population density Road function
Road repairs Alternative route Social/Population Physical 3 Economy Environment Land slope
Land use Population density Settlements Infrastructure Economy Socio-economic Housing density National road
Traffic intensity Capacity Social and scientific aspects Local community Adaptation Multi-sector approach
Long-term planning Geographic Information System
(GIS)
Overlay
3 Spatial analysis Zone mapping Road repairs Region
Preparedness Soil and water conservation 2 Disaster mitigation Early warning Regulations Society Society Regulations Intervention area
Road
Road infrastructure Population Transportation system
Government Settlements Agricultural land
Road infrastructure Drainage system
Slope protection Land use Population density Economic value Road section
Maintenance Population density 2 Society Settlements Society Risk zoning mapping Mitigation Mapping of landslide zones Road construction Drainage system
Risk map
Spatial data
Social
Physical Geology Social
Mitigation Agroforestry techniques Land conservation Mitigation techniques Social
Physical Economy Institutions Rainfall Soil conditions Settlement
Agricultural land
Rainfall Slope gradient
Soil type
Land use Fault/Fracture Vegetative Density Rainfall Slope gradient
Fault activity Land cover
Land conversion River bank erosion Rainfall Topography Active seismic
Climate change Landslide intensity Seismic
Rainfall Geological conditions Soil conditions 4 Slope gradient
Rainfall Slope gradient
Soil type
Social
Physical Economy Environment Road
Mitigation Disaster management Training and socialization Early warning system
Settlement planning Society Population density Solid buildings Residential land
Settlement
Population growth
Land use Age
Level of education Unemployment Settlements Infrastructure Economy Settlement
Public facilities Agricultural activities Evacuation route Facilities and infrastructure Disaster socialization Infrastructure Evacuation access
Spatial planning Infrastructure Mitigation system
Disaster education Early warning Adaptation strategy Policy Landslide mapping Public education Population density Infrastructure Geographic Information System
(GIS)
2 Mitigation International Conference on Engineering.
Applied Science And Technology e-ISSN : 3110 - 3154 Figure 4.
Research Variables Used (Researcher, 2.
The Landslide Risk Level Evaluation in South Sumatra Province Reffanda Kurniawan.
Heni Firiani .
Yulindasari Sutejo.
Bimo Brata Adhitya International Conference on Engineering.
Applied Science And Technology 2025 Landslide risk level variables are factors that influence the likelihood of landslides occurring and their potential impact.
These variables are generally divided into three main components, namely:
hazard, vulnerability, and capacity.
Understanding landslide risk level variables is essential as it provides a scientific and practical basis for understanding, mapping, and controlling landslide Knowing the variables of landslide risk level is an important step in understanding, predicting, and reducing the impact of landslides in an area.
Landslides are geological disasters triggered by various factors, both natural and human-induced.
Therefore, identifying influential variables such as slope inclination, soil type, rainfall, land cover, and geological conditions is the basis for assessing the level of danger in an area.
Understanding these variables enables researchers and authorities to identify the main causes of landslides and map areas that are prone to similar events in the future.
In addition, knowledge of vulnerability variables such as population density, infrastructure value, and socio-economic conditions of the community is also necessary to assess the potential impact of a Meanwhile, capacity variables, such as the availability of mitigation infrastructure, early warning systems, and community preparedness levels, play an important role in determining the extent to which an area is able to cope with such By combining these three groups of variables, the level of landslide risk can be analyzed Information on landslide risk level variables is crucial in compiling landslide-prone zone maps based on geographic information systems (GIS), which can be used to support spatial planning policies and sustainable development planning.
Through this approach, high-risk areas can be identified more accurately so that mitigation measures, such as the construction of slope drainage systems, planting of erosion-control vegetation, and relocation of settlements, can be designed more In addition, understanding risk variables also contributes to increasing public awareness in recognizing the early signs of landslides and taking preventive action.
Thus, knowledge of landslide risk variables is not only important academically, but also has strategic value in disaster risk reduction efforts and the protection of human safety and environmental assets.
Conclusions Disaster risk assessment is an effort to produce information related to the level of disaster risk in an The level of risk is obtained from a combination of three components, namely hazard, vulnerability and capacity.
These three components are determined based on their respective parameters.
Based on this summary, the researchers used the landslide risk level influenced by hazard, vulnerability, and capacity.
The hazard variables include nine variables, namely rainfall, geology, land use, slope, soil type, soil bearing capacity around bridges, safety factors, zone typology, and landslide history and frequency of occurrence around road sections.
The vulnerability risk level variables consist of physical, social, economic, and environmental Physical vulnerability includes: .
existing road conditions, .
existing bridge conditions, .
road terrain classification, .
number of bridges on the road section, .
availability of alternative routes/road sections, .
slope drainage, .
slope/cliff protection structures, .
availability of drainage systems, .
road section length, and .
total bridge length.
Social vulnerability is the population .
Economic vulnerability is: .
Regional Gross Domestic Product (RGDP) of the province/district/city on the road section, and .
the function of the city in economic development.
Environmental vulnerability is the result of human activities such as the creation of ponds on road slopes.
The variables for landslide risk level from the risk capacity factor consist of: .
social, .
economic, and .
The landslide risk level from the social capacity factor is: .
institutional readiness for emergency response, and .
recovery of resource availability and completeness.
The landslide risk level from economic capacity factors is: assessment of Regional Gross Domestic Product (PDRB).
The landslide risk level from institutional capacity factors is access and mobility of resources.
Evaluation of landslide risk levels is important for the safety of lives, property, and the environment because it helps identify hazards, assess vulnerability, and predict losses.
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