Landslide Disaster Mitigation Research Articles

The Identification and Influence Factor Analysis of Landslides Using SBAS-InSAR Technique: A Case Study of Hongya Village, China

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.

A Small-Scale Landslide in 2023, Leshan, China: Basic Characteristics, Kinematic Process and Cause Analysis

Sudden mountain landslides can pose substantial threats to human lives and property. On 4 June 2023, a landslide occurred in Jinkouhe District, Leshan City, Sichuan Province, resulting in 19 deaths and 5 injuries. This study, drawing on field investigations, geological data, and historical imagery, elucidates the characteristics and causes of the landslide and conducts a reverse analysis of the landslide movement process using Massflow V2.8 numerical simulation software. The results indicate that rainfall and human engineering activities are key factors that triggered this landslide. Numerical simulation shows that the landslide stopped after 60 s of sliding, with a movement distance of approximately 286 m, a maximum sliding speed of 17 m/s, and a maximum accumulation thickness of 7 m, eventually forming a loose landslide debris accumulation of approximately 5.25 × 103 m3. The findings of this study provide significant reference value for research on landslide movement characteristics and disaster prevention and mitigation in mountainous areas.

Landslide Disaster Mitigation Based on Community Local Wisdom Values in Lebong Regency

Landslide Disaster Mitigation Based on Community Local Wisdom Values in Lebong Regency

Utilization of satellite data for landslide disaster mitigation in extreme event: a case study June 18, 2016, landslide in Banjarnegara Regency, Indonesia

Abstract Understanding the characteristics of landslides produced by rainfall and soil humidity is critical for reducing the occurrence of landslides. Extreme rain, as an external impact, has been suggested to play a role in landslide development. Many people were killed in a landslide that occurred in the Banjarnegara Regency on June 18, 2016, which was unexpected because June is not Indonesia’s rainy season. El Niño and La Niña events were observed in Indonesia in 2015-2016. The initial hypothesis is that such extreme rain conditions caused this landslide. This study aims to apply an alternative approach to detecting landslides by monitoring soil moisture and rainfall with remote sensing data during extreme events. An analysis of precipitation and moisture data has been carried out before the event and is associated with the El Niño and La Niña phenomena. The findings indicate that the natural environment of Banjarnegara Regency is unique. Landslides occur even during the extreme dry season. Rain still drops, and soil moisture is high. The impact of local variables is more significant than regional conditions. They are still at risk of a landslide during the El Niño phase. During that stage, Banjarnegara Regency’s potential landslide area would continue to be observed. Monitoring needs to be carried out rapidly, with a high level of spatial and temporal resolution. It is possible to resolve it using IMERG and SMAP data.

Integrated Landslide Mitigation Model in Physics Concept of Environmental Pollution and Alternative Energy

This research integrates landslide disaster mitigation into physics education, with a focus on environmental pollution and alternative energy for high school students. The research aims to address the frequent landslides in Indonesia, particularly in Central Bengkulu Regency, which can cause significant disruptions. The objective is to develop a model for integrating disaster mitigation into physics education at MAN Insan Cendekia Central Bengkulu. The research method employed is Research and Development (R&D) using the 4D model, limited to the design stage. The research subjects were students in class XI-1 specializing in Physics at MAN Insan Cendekia. The analysis of disaster potential in Central Bengkulu indicates a need for disaster mitigation education. The Butterfly Webbed Concept Map was developed to integrate physics material with disaster mitigation education. It provides a visual representation of the relationship between environmental pollution, alternative energy, resource utilization, and landslide disaster mitigation. This integration can be applied in physics learning for disaster risk awareness for students.

Research on a Multi-Dimensional Indicator Assessment Model for Evaluating Landslide Risk near Large Alpine Reservoirs

Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas.

Implemention of system landslide disaster mitigation based internet of things

Implemention of system landslide disaster mitigation based internet of things

Comparison of Statistical and Machine-Learning Model for Analyzing Landslide Susceptibility in Sumedang Area, Indonesia

Landslides have produced several recurrent dangers, including losses of life and property, losses of agricultural land, erosion, population relocation, and others. Landslide mitigation is critical since population and economic expansion are rapidly followed by significant infrastructure development, increasing the risk of catastrophes. At an early stage in landslide-disaster mitigation, landslide-risk mapping must give critical information to help policies limit the potential for landslide damage. This study will utilize the comparative frequency ratio (FR) and random forest (RF) techniques; they will be utilized to properly investigate the distribution of flood vulnerability in the Sumedang area. This study has identified 12 criteria for developing a landslide-susceptibility model in the research region based on the features of past disasters in the research area. The FR and RF models scored 88 and 81% of the AUC value, respectively. Based on the McNemar test, the FR and RF models featured the same performance in determining the landslide-vulnerability level performances in Sumedang. They performed well in assessing landslides in the research region; therefore, they may be used as references in landslide prevention and references in future regional development plans by the stakeholders.

Efforts to Increase the Capacity of Sukorejo Village, Semarang City Community in Facing Landslide Disasters Through the Preparation of Evacuation Standard Operating Procedures (SOPs)

This study identified the problem of landslide disasters that often occur in Sukorejo Village, Gunungpati District, Semarang City. Sukorejo village belongs to the high ground movement zone which has a high degree of vulnerability to soil movement. The purpose of the study was to determine efforts to increase the capacity of the community in facing landslide disasters through the preparation of evacuation standard operating procedures (SOPs). The data collection of this study used interview, observation, documentation, and geographic information systems (GIS) methods. Data analysis is carried out with the approach of spatial analysis and qualitative descriptive analysis. The results showed that the preparation of evacuation SOPs is very important to increase the capacity of the community during landslide emergency response. The recommendations given are the need to add an Early Warning System (EWS) tool, install evacuation route signs, and gathering points in landslide-prone areas. In addition, the community also needs to increase awareness and preparedness in the face of possible landslides. This research contributes to improving understanding of landslide disaster mitigation efforts and can be followed up by assessing community capacity and evaluating future evacuation SOPs.

Effectiveness of landslide mitigation education on disaster knowledge and preparedness of students in class VIII SMPN 1 Jenawi

The implementation of landslide disaster mitigation education for students is an effort that can be made to increase students’ disaster knowledge and preparedness for landslides. Actions are needed to increase disaster knowledge and preparedness to minimize the impact caused by disasters. The objectives of this study are (1) to analyze the level of disaster knowledge of grade VIII students towards landslides; (2) to analyze the preparedness of grade VIII students of SMP Negeri 1 Jenawi towards landslides; and (3) to analyze the effectiveness of the implementation of landslide disaster mitigation education on the level of disaster knowledge and the level of preparedness of grade VIII students of SMP Negeri 1 Jenawi. This quantitative research method uses a pretest-posttest control group experimental design that uses questionnaires for data collection. The results showed that 1) The level of disaster knowledge of experimental class students on pretest results has an average of 81.59, while the control class has an average of 79.71, 2) The level of preparedness of experimental class students has an average of 64.97, while the control class has an average of 66.52. 3) The effectiveness of implementing disaster mitigation education on the level of disaster knowledge and preparedness of students in the N-Gain Score test shows that it is effective enough to be applied as an appropriate and effective step in reducing the risk of landslides.

LoRa Network Performance Analysis on Landslide Monitor for Landslide Disaster Mitigation in the Greater Malang Area

LoRa Network Performance Analysis on Landslide Monitor for Landslide Disaster Mitigation in the Greater Malang Area

Socialization of Flood and Landslide Disaster Mitigation as a Lesson for Elementary School Students in Cikondang Village, Hantara District, Kuningan Regency

Socialization of Flood and Landslide Disaster Mitigation as a Lesson for Elementary School Students in Cikondang Village, Hantara District, Kuningan Regency

Research on landslide susceptibility prediction model based on LSTM-RF-MDBN.

The occurrence of landslide disasters causes huge economic losses and casualties. Although many achievements have been made in predicting the probability of landslide disasters, various factors such as the scale and spatial location of landslide geological disasters should still be fully considered. Further research on how to quantitatively characterize the susceptibility of landslide geological disasters is necessarily important. To this end, taking the Wenchuan earthquake as the research area and extracting eight influencing factors, including terrain information entropy (Ht), lithology, distance from rivers, distance from faults, vegetation coverage (NDVI), distance from roads, peak ground motion acceleration (PGA), and annual rainfall, a landslide susceptibility prediction model was hereby established based on LSTM-RF-MDBN, a landslide susceptibility prediction map was drawn, and the spatial distribution characteristics of landslide disasters were analyzed. The results showed that (1) LSTM had good prediction results for the eight influencing factors, with an average prediction accuracy of 85%; (2) compared with models such as DNN and LR for predicting landslide disaster points, the AUC value of RF for predicting landslide point positions reached 0.88, presenting a higher accuracy compared to other models; (3) the AUC value of the landslide susceptibility prediction model based on LSTM-RF-MDBN reached 0.965, which had a high accuracy in predicting landslide susceptibility. Overall, the research results can provide a scientific basis for selecting the best strategy for landslide disaster warning, prevention, and mitigation.

Landslide disaster mitigation and adaptation strategy in one of the East Java horseshoe areas using geographic information system analysis

The horseshoe area of East Java is an area that has abundant natural resource potential. In the northern part of the horseshoe region there is a very good biological natural resource with the potential to be used as a food source. Meanwhile, in the southern part is rich with biological and mining potential. This potential causes changes in land use in the northern and southern regions which can trigger landslides in the Horseshoe area. Changes in land use that change the topography and vegetation to become steep and barren will have a very significant impact on landslides. This study aims to map and predict areas affected by erosions using environmental and physical data using Geographic Information System (GIS) Analysis. The results of the mapping are areas that have threats and are vulnerable as well as capacity to erosion. Based on the results of the mapping, it shows that the index of threats greatly influences the hazard of landslides that occur in the area. Based on the result of the mapping, it shows that the disaster mitigation and adaptation strategy is to reduce the threat index or very high vulnerabilities that affect the risk of landslides that occur in an area.

Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China

Abstract. To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.

Landslide-prone areas in Makale Selatan with the analytical hierarchy process method

Landslides are one of the most common natural disasters in Indonesia. The Tana Toraja area has not been spared landslides, especially in the Makale Selatan district. Makale Selatan is one of the supply areas for agricultural products in South Sulawesi. Therefore it is necessary to map landslide-prone areas to support landslide disaster mitigation. This study aims to map landslide-prone regions and determine the factors most influencing landslide events in Makale Selatan. This study uses an Analytical Hierarchy Process (AHP) method with a weighting factor. The parameters, namely; slope, rainfall, land use, soil texture, soil type, and rock formations. The results showed that the incidence of landslides in Makale Selatan was strongly influenced by the slope, with the highest weighting factor with a weight of 0.410 (Score of 41%). Rainfall and land use with the same weight of 0.170 (Score of 17%), then the parameters of soil type and soil texture also with the exact weight is 0.096 (score of 9.6%), and the rock formation parameters with a weight of 0.058 (score 5.8%). The level of landslide-prone areas in the study area namely; 12.46% for the not prone area, 51.72% for slightly prone, prone areas 25.72%, and very prone was 10.11% for the total study area. The not-prone village is Tiromanda, and the most prone village is Lembang Randan Batu. The slope parameter is the parameter that most influences landslides in Makale Selatan. Land use for agriculture must be mechanically conserved to reduce the slope angle and prevent landslides.

Edukasi Sadar Bencana Melalui Transfer Knowledge Sebagai Upaya Peningkatan Pengetahuan Masyarakat Terhadap Mitigasi Bencana

Natural disasters from year to year become records as well as data on how natural disasters are very detrimental to the survival of mankind as a history, therefore the need for disaster preparedness as an anticipation in disaster management. The research approach used is a descriptive qualitative approach which aims to analyze and describe disasters and disaster mitigation in an area. This study is motivated by the fact that in Nagari Pariangan there is a low level of public knowledge about disaster mitigation. In addition, there has been no presentation of material on mitigation of volcanic eruptions and landslides in disaster-prone areas, so the role of the government and students is needed to transfer material knowledge about mitigation to the community. This study aims to develop volcano and landslide disaster mitigation efforts for the people of Nagari Pariangan, Pariangan District, Pariangan Regency in the form of disaster mitigation education and awareness activities.

Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province

Qinghai is rich in mineral resources, but frequent and large-scale mineral mining has caused secondary damage to the fragile primary surface and produced a large number of landslide disasters. In complex geological environments such as glacier ablation and frequent tectonic movements, a complete quantitative evaluation method for landslide risk in high-cold mining areas has not yet been formed. In view of this, this article uses the field survey and remote sensing data of the Datong mining area in Qinghai Province in 2012 as the basic data. We comprehensively considered five first-level factors (13 s-level factors) including topography, lithological structure, mining engineering activities, land use, and dynamic deformation as evaluation indicators for landslide susceptibility in mining areas, and used the Topographic Wetness Index (TWI) and the Human Engineering Activity Intensity (HEAI) to quantitatively estimate the hazard of landslide according to the landslide trigger mechanism. The weight-of-evidence approach was used for landslide hazard and risk mapping under different landslide--inducing conditions. The results indicate that the extremely high-hazard areas induced by human engineering activities account for 14% of the total area, and the extremely high-risk areas account for 13% of the total area in the Datong mining area, and the area of the extremely high-risk area is large; the landslide risk assessment mapping model constructed in this study can effectively evaluate the probability of slope instability caused by rainfall and human engineering activities. The effective value of the receiver operating characteristic (ROC) curve of the sensitivity assessment model reaches 0.863, and the evaluation results are consistent with reality; using the weight-of-evidence model for landslide risk assessment is more in line with the actual situation in alpine mining areas, and is more suitable for guiding landslide risk management and disaster prevention and mitigation in mining areas.

INCREASING PUBLIC KNOWLEDGE THROUGH HEALTH EDUCATION ON RADIO THROUGH TALKSHOWS WITH TITLE “THE ROLE OF VOLUNTEERS IN HELPING COMMUNITIES AFFECTED IN DISASTER” IN PROGRAM 1 RRI MALANG

Volunteers are individuals who voluntarily donate their time and effort to make a positive contribution to others without expecting financial or material rewards. The roles of volunteers before a disaster (pre-disaster) is as supporters in policy formulation, planning, disaster risk reduction, and community disaster prevention and preparedness. This community service (PKM) aims to provide education about the role of volunteers, especially health volunteers, in helping people affected by disasters. The PKM activity was aimed at listeners of the Kiprah Indonesia program, which was broadcasted on Program 1 of the LPP RRI Malang Culture Channel. This PKM activity took the form of a talk show held live at the Kiprah Indonesia event on the Culture Channel Program 1 RRI Malang 105.3 FM, and was also broadcast via live streaming on the official RRI Malang Youtube channel. The community service (PKM) was carried out on December 29, 2022, by broadcasting live on the Kiprah Indonesia event on Program 1 Culture Channel LPP RRI Malang 105.3 FM. The PKM program was a radio talk show. Health education provided by this radio talk show was expected to increase public knowledge about the disaster management cycle, the definition/understanding of volunteers in disaster management, the working principles of volunteers, the five pillars of volunteers in disaster management, the task/duties of volunteer organizations, volunteer management, and also landslide and flood disaster mitigation. This talskhow also aims to provide mitigation and preparedness measures to deal with disasters, especially floods and landslides

Comparison of two landslide hazard zonation methods in the volcanic terrain of Temanggung Regency, Central Java, Indonesia

Landslides are a recurring phenomenon that disrupts the natural environment and causes yearly property damage, economic losses, and fatalities. The damage is expected to increase due to deforestation rates, population growth, agriculture, slope-building infrastructure expansion, and global climate change. This study assesses the susceptibility to landslides through Weight of Evidence (WoE) and Frequency Ratio (FR) methods in the Temanggung Regency, Central Java Province, Indonesia, that located on the slopes of two active volcanoes. Initially, a landslide record and the input parameters of the landslide controlling factors were prepared from field surveys, remote sensing data, and secondary data and processed by a geographic information system (GIS). Six landslide parameters in thematic layer maps were selected to develop landslide susceptibility: slope, lithology type, geological structure density, land cover, and rainfall. According to the WoE and FR models, a landslide susceptibility zoning map was classified into four landslide-prone zones from low to very high. Finally, the success and predictive rate curves method confirmed the landslide susceptibility maps to check the model accuracy. The results showed that the landslide susceptibility map using the WoE method had better accuracy than the FR method, with a success rate of 78.48% and a prediction rate of 81.1%. In comparison, the FR method was 74.53% for the success rate and 78.48% for the prediction rate. These landslide susceptibility maps can be used as a guideline to develop land-use planning and landslide disaster mitigation.