Landslide Disaster Research Articles

Landslide risk evaluation method of open-pit mine based on numerical simulation of large deformation of landslide

It is of great practical significance to carry out quantitative risk assessment of landslide disaster to protect people’s lives and property safety and maintain the sustainable development of social economy. The delineation of landslide disaster range is the key link of landslide risk evaluation in open-pit mine. This study took the open-pit coal mine coal mine in Block I of Thar coal field in Pakistan as the research background, and constructed a framework for landslide risk evaluation in open-pit mines. Based on the numerical simulation method of large deformation of landslide, the disaster range of landslide in open-pit mine was delineated. The stability of stope slope was analyzed based on rigid body limit equilibrium method. The probability of landslide instability was calculated based on Monte Carlo method. And the comprehensive fuzzy evaluation model was established to calculate the total risk value of landslide. The results showed that: through numerical simulation and empirical formula calculation, the landslide disaster range was accurately delineated, and the comprehensive vulnerability of the disaster bearing body was determined to be medium vulnerability; the annual probability of landslide instability under natural conditions was 0.003, which belongs to moderate risk. The annual probability of landslide instability under rainstorm conditions was 0.024, which was highly dangerous; the annual probability of landslide instability under seismic conditions was 0.018, which was highly dangerous. Under natural conditions, the total risk value of landslide was 114,686.4 rmb, and the annual mortality rate of population was 0.0255 people/year. The total risk value of landslide under rainstorm condition was 11,707,570 rmb, and the annual mortality rate of population was 0.18375 people/year. The total risk value of landslide under earthquake condition was 43,007,400 rmb, and the annual mortality rate of population was 0.135 person/year, which was an unacceptable risk. The economic loss was a small geological disaster risk under natural conditions, and it was a medium-sized geological disaster risk under both rainstorm and earthquake conditions. Therefore, landslide prevention and control and management measures such as slope deformation monitoring were proposed to ensure the safety of personnel and property in open-pit mines.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience driving or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities. And 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. There are five machine learning methods (Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)) were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.

Analysis of Disaster Risk on Expressways for Foreign Tourists to Japan Using Rental Cars: Study Using Central Nippon Expressway Pass

The travel patterns of foreign visitors to Japan are changing from group travel, which was the mainstream to date, to individual travel, and there has been an increase in the number of cars rented by visitors as a means of transportation. The use of rental cars improves access to rural areas and is being promoted as a means of transportation. However, if a natural disaster occurs while driving a rental car, then the individual may face difficulties owing to a lack of local support. Therefore, the traffic volume on the roads used by foreign visitors to Japan during certain periods needs to be estimated in advance as well as the natural disaster risks that exist on those roads. In this study, we utilized CEP data managed by the Central Nippon Expressway Company in Japan to identify the risk of disaster for foreign visitors to Japan who use expressways with rental cars. The CEP data has usage records of foreign visitors to Japan who used rental cars on expressways in the Chubu region of Japan during the period from 1 April 2018 to 31 March 2019. Using this data, we calculated the risk of earthquake and landslide disasters. Result of this analysis, it is clear that the risk of earthquake disasters is high on expressways in the Tokai region, and the risk of landslide disasters is high on expressways in mountainous areas.

LEGAL SECURITY OF OWNERSHIP OF LAND CERTIFICATES POST EXCHANGING FOREST AREA FOR RESIDENTS RELOCATION IN NGROTO DUSUN, WONOGIRI, CENTRAL JAVA

This study aims to discuss the description of the implementation process of exchanging forest areas for the relocation of residents and legal certainty of ownership of community land certificates after the relocation of community forest area exchanges in Dusun Ngroto. The research method used in this research is juridical-empirical descriptive by using qualitative data analysis methods to review literature and events that actually occurred through observation and interviews. The research location is in Ngroto Hamlet, Pare Village, Selogiri District, Wonogiri Regency. The results of the discussion found that 1 )The background to the relocation was due to the fact that in March 1999 in the Ngroto Hamlet area a landslide occurred resulting in as many as 43 family cards (115 people), supporting factors for relocation including social relations, economy, community involvement and participation with the government while the inhibiting factors are infrastructure, policies that underlie relocation based on humanitarian considerations. 2) Legal certainty of ownership of land certificates after the relocation residents have not obtained the rights to the land they occupy regarding evidence of land certificates, the Wonogiri Regency Government is in the process of eliminating the land ownership rights of residents who have been victims of the landslide disaster 11.66 Ha (Eleven and Sixty Six One Hundred Hectares) with a replacement land obligation of 13.11 Ha (Thirte

Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions

Climate change can exacerbate the occurrence of extreme precipitation events, thereby affecting both the frequency and intensity of rainfall-induced landslides. It is important to study the threat of rainfall-induced landslides under future climate conditions for the formulation of disaster prevention and mitigation policies. Due to the complexity of the climate system, there is great uncertainty in the climate variables simulated by a global climate model (GCM), which will be further propagated in landslide prediction. In this study, we investigate the spatial and temporal trends of future landslide hazards in China under climate change, using data from a multi-model ensemble of GCMs based on two scenarios, RCP4.5 and RCP8.5. The uncertainty characteristics are then estimated based on signal-to-noise ratios (SNRs) and the ratio of agreement in sign (RAS). The results show that the uncertainty of landslide prediction is mainly dominated by the GCM ensemble and the RCP scenario settings. Spatially, the uncertainty of landslide prediction is high in the western areas of China and low in the eastern areas of China. Temporally, the uncertainty of landslide prediction is evolving, with characteristics of high uncertainty in the near future and characteristics of low uncertainty in the distant future. The annual average SNRs in the 21st century are 0.44 and 0.50 in RCP4.5 and RCP8.5, respectively, and the RAS of landslide prediction in Southeastern China is only 50–60%. This indicates that more than half of the patterns show trends that are opposite to those of the ensemble, suggesting that their landslide change trends are not universally recognized in the pattern ensemble. Considering the uncertainty of climate change in landslide prediction can enable studies to provide a more comprehensive picture of the possible range of future landslide changes, effectively improving the reliability of landslide hazard prediction and disaster prevention.

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.

Identification of Landslides Controlling and Triggering Factors using the Analytical Hierarchy Process (AHP) in Dermasuci Village, Pangkah District, Tegal Regency, Central Java Province - Indonesia

The National Disaster Management Agency (BNPB) stated that in 2022 around 634 landslides occurred in Indonesia. One of the regions in Indonesia that experienced landslide disasters is Dermasuci Village, Pangkah District, Tegal Regency, Central Java Province. The first landslide disaster occurred on February 5, 2022, and the second landslide occurred on February 10, 2022. The landslide disasters caused damage to 61 residents’ houses and four road sections. The objective of this study is to identify the controlling and triggering factors that mainly contributed to the landslide event. These two factors are determined based on the order of their static and dynamic properties using the analytical hierarchy process (AHP) method. Controlling and triggering factors of the landslide in the study area are determined based on geological and engineering geological conditions which were obtained from surface mapping results. The controlling factors obtained are the rock bedding inclination, geological structure in the form of faults, slope inclination, engineering properties of soil and rock, land use, and groundwater table. The triggering factors that are known and commonly occur are rainfall in the form of antecedents and intensity, and earthquakes that can induce landslides.

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.

Multi-source monitoring data helps revealing and quantifying the excavation-induced deterioration of rock mass

There exist many high-steep rock slopes in Southwest China due to the construction of large-scale hydropower projects. The study on excavation-induced damage of high rock slopes is vital for disaster prevention and mitigation. Due to the complexity of high-steep rock slopes, assessing the excavation-induced deterioration of rock mass is often challenging. This study used the space-borne Interferometric synthetic aperture radar (InSAR) and multipoint displacement meters to monitor the slope displacements before and after the excavation. Based on the multi-source monitoring data and the parameter estimation method, we evaluated the changes in rock mass properties due to slope excavation. We employed an improved particle swarm optimization algorithm with dynamic topology and adaptive parameter adjustment and a neural network-based surrogate model in the parameter estimation. The degradation of rock mass strength and stiffness coincides with the increased sensitivity of slope deformation to rainfall infiltration. An in-depth analysis indicates that the absence of vegetation protection and excavation-induced deterioration of rock masses are responsible for the noticeable slope deformation during the rainy season. As slope excavation profoundly influences slope stability and deformation, we should pay closer attention to the excavation way and support style. This study also demonstrates the benefits of using multi-source monitoring data in slope deformation analysis. The findings can aid in comprehending the deformation evolution and instability mechanisms of rock slopes excavated at the hydropower sites, thereby contributing to the prevention and mitigation of landslide disasters.

Early detection of potential landslides along high‐speed railway lines: A pressing issue

AbstractEarly detection of landslides is important for prevention and mitigation of landslide disasters. Especially, accurately identifying potential landslides along high‐speed railway is becoming a pressing issue for operation safety of high‐speed railway. Here, 161 Sentinel‐1A satellite images from March 2017 to September 2022 were acquired to detect potential landslides along the Lanzhou‐Urumqi high‐speed railway (LUHR) in Qinghai. Results show that rainfall is the main cause for the transition from secondary creep (steady state) to tertiary creep (accelerated) at the Jiujiawan landslide. The hot spot analysis method was used to cluster the average line‐of‐sight deformation velocity along the LUHR, effectively extracting five deformation zones that may pose a threat. Optical satellite image analysis revealed four potential landslides and one unstable slope. Furthermore, the rescaled range analysis documented that the Hurst exponent is greater than 0.8 for all four potential landslide instability points, implying that the identified potential landslide areas are still in motion and may pose a threat to the operation of the LUHR. On this basis, we propose a framework for early identification and monitoring of landslides along the high‐speed railway lines that supports landslide hazard managing with the goal to reduce landslide risk.

Research on Performance Test of the Optic-Electric Sensors for Reservoir Landslide Temperature Field Monitoring

In recent years, with the superposition of extreme climate, earthquakes, engineering disturbance and other effects, global landslide disasters occur frequently. Due to reservoir landslides being mostly in a multi-field coupling environment, the temperature field will impact the deformation and seepage fields, thereby affecting the stability of the reservoir landslide. The variation in the landslide’s surface temperature also directly affects the stress and deformation of deep rock masses. If hidden dangers are not detected in time, and corresponding measures are implemented, it is easy to cause landslide instability. In order to clarify the temperature measurement performance of different optic-electric sensors and the application characteristics of layout techniques, laboratory calibration tests of temperature sensors under different adhesives and attachment materials are carried out in this paper. It was found that the test data of the iron bar had the best effect among the four attachment materials overall. Therefore, the bar with a high-stiffness material should be preferred when selecting a pipe fitting as the fiber Bragg grating (FBG) temperature attachment in the borehole. However, considering the high requirements for the durability of sensors and layout techniques in on-site monitoring, the long-term stability of the adhesives used in actual monitoring needs to be improved. At the same time, it was found that the platinum 100 (PT100) temperature sensor has relatively higher testing accuracy (A: 0.15 + 0.002 × |t|; B: 0.30 + 0.005 × |t|), a larger temperature measurement range (−200~+850 °C) and better temperature measurement stability when compared to conventional sensors. Moreover, its resistance value has a good linear relationship with temperature. Finally, the Xinpu landslide in the Three Gorges Reservoir area was selected as the research object for on-site monitoring. There was a high correlation between the on-site monitoring results with the laboratory calibration test results. Therefore, through the performance test of optic-electric sensors in reservoir landslide temperature fields, more accurate solutions can be provided for selecting sensors and designing layout techniques to monitor the underground temperature field of landslides under different geological conditions. Thereby, grasping the real-time state information of the reservoir landslide temperature field is achieved accurately, providing an important reference for early warning, prediction, prevention and the control of reservoir landslide disasters.

Development of Physical Vulnerability Curve based on Debris Flow Events

In recent years, owing to climate change various slope disasters are rising. Debris flow causes large damage to human life and social infrastructure. Generally, the vulnerability assessment of slope disasters is carried out using vulnerability curve, which consists of hazard intensity and damage index. A vulnerability curve needs to be highly reliable. In this study, GIS-based topographical characteristics and the information about 27 debris flow events that occurred between 2011 and 2020 were collected and analyzed. The hazard intensity of the debris flow events was evaluated considering entrainment of soils and buildings. Two types of buildings, non-reinforced concrete structure (non-RC) and reinforced concrete structure (RC frame) were classified into four types of vulnerability indices (slight, moderate, extensive, and complete) based on the degree of damage to the building. The relationship between the hazard intensity and the degree of damage to the building was studied through nonlinear regression analysis to derive a vulnerability curve according to the hazard intensity. The proposed vulnerability curve can be used as fundamental data for the vulnerability assessment of landslide disasters.

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 Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example

Shuangbai County, located in Yunnan Province, Southwest China, possesses a complex and diverse geological environment and experiences frequent landslide disasters. As a significant area for disaster prevention and control, it is crucial to assess the susceptibility of landslides for effective geological disaster prevention, urban planning, and development. This research focuses on eleven influencing factors, including elevation, slope, slope direction, rainfall, NDVI, and distance from faults, selected as evaluation indexes. The assessment model is constructed using the information quantity method and the information quantity logistic regression coupling method to analyze the landslide susceptibility in Shuangbai County. The entire region’s landslide susceptibility is classified into four categories: not likely to occur, low susceptibility, medium susceptibility, and high susceptibility. The accuracy and reasonableness of the models are tested and compared. The results indicate that the coupled information–logistic regression model (80.0% accuracy) outperforms the single information model (74.2% accuracy). Moreover, the density of disaster points in the high-susceptibility area of the coupled model is higher, making it more reasonable. Thus, this model can serve as a valuable tool for evaluating regional landslide susceptibility in Shuangbai County and as a basis for disaster mitigation planning by relevant authorities.

Penerapan Sistem Informasi Geografis (SIG) Dalam Mengidentifikasi Potensi Kelongsoran

The primary objective of this study was to enhance the community's understanding and awareness of landslide risks and mitigation strategies by employing Geographic Information Systems (GIS) to identify landslide-prone areas in the Magetan District, East Java. The initiative unfolded in several stages, which included: (1) emphasizing the importance of mapping areas susceptible to landslides; (2) consolidating data through satellite image analysis and GIS; (3) developing GIS-based maps that depict landslide-prone areas; and (4) circulating these maps among the local community and governing bodies. In conclusion, this study demonstrates that a participatory approach, combined with advanced technologies such as GIS, can play a pivotal role in disaster mitigation. Early identification of landslide risks and appropriate mitigation strategies using GIS and community education can significantly enhance community preparedness and resilience to landslides. The potential applicability of this method to other regions with similar topographical and geological conditions underscores its relevance in improving community readiness and resilience against landslide disasters.

Landslide Disaster Leading to an Unusual Case of Polytrauma with Uterine Rupture and Fetal Decapitation in Early Pregnancy

Landslide Disaster Leading to an Unusual Case of Polytrauma with Uterine Rupture and Fetal Decapitation in Early Pregnancy

A landslide susceptibility assessment method based on auto-encoder improved deep belief network

Abstract The landslide susceptibility assessment is an essential part of landslide disaster risk identification and prevention. However, the binarization of the hidden layer limits the parameterization ability of the conditional probability of visible layer, making the training process of restricted Boltzmann machine more difficult and further limiting the accuracy and efficiency of deep belief network (DBN) model in landslide susceptibility assessment. Therefore, this study proposed a landslide susceptibility assessment method based on Auto-Encoder (AE)-modified DBN. Zhouqu County, Gansu Province in the People’s Republic of China, was selected as the study area. Historical landslides in Zhouqu County were identified using small baseline subset interferometric synthetic aperture radar technology and optical image. Landslide factors were screened based on a geographical detector and stepwise regression method. The Logcosh loss function and determinant coefficient R 2 index were used to evaluate the training process of the AE model, and the balanced cross entropy loss function was used to evaluate the entire network training process. In addition, the area under the curve (AUC) of the synthetical index model (SIM), support vector machine (SVM), and multilayer perceptron (MLP) were compared and evaluated. The results indicated that the proposed model could significantly improve the accuracy of landslide susceptibility assessment. The AUC value of the proposed model was 0.31, 0.12, and 0.11 higher than that of SIM, SVM, and MLP, respectively. Therefore, the improved DBN model based on AE proposed is reliable for early landslide identification and prediction.

Rapid Landslide Extraction from High-Resolution Remote Sensing Images Using SHAP-OPT-XGBoost

Landslides, the second largest geological hazard after earthquakes, result in significant loss of life and property. Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, China, is a typical landslide-prone area in the Three Gorges Reservoir Area. In this study, we newly integrate Shapley Additive Explanation (SHAP) and Optuna (OPT) hyperparameter tuning into four basic machine learning algorithms: Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Additive Boosting (AdaBoost). We construct four new models (SHAP-OPT-GBDT, SHAP-OPT-XGBoost, SHAP-OPT-LightGBM, and SHAP-OPT-AdaBoost) and apply the four new models to landslide extraction for the first time. Firstly, high-resolution remote sensing images were preprocessed, landslide and non-landslide samples were constructed, and an initial feature set with 48 features was built. Secondly, SHAP was used to select features with significant contributions, and the important features were selected. Finally, Optuna, the Bayesian optimization technique, was utilized to automatically select the basic models’ best hyperparameters. The experimental results show that the accuracy (ACC) of these four SHAP-OPT models was above 92% and the training time was less than 1.3 s using mediocre computational hardware. Furthermore, SHAP-OPT-XGBoost achieved the highest accuracy (96.26%). Landslide distribution information in Fengjie County from 2013 to 2020 can be extracted by SHAP-OPT-XGBoost accurately and quickly.

Analysis of Social and Environmental Vulnerability Against Landslides Using Scoring and Weighting Methods at Probolinggo District

Landslides phenomena majority occur in area which is adjacent with mountain or hill where the soil and rock structure move down the slopes. It is caused by land instability such as loss of plants or trees in highland which have function of binding soil grains while maintaining pores of soil beneath. Based on data from the Regional Disaster Management Agency (BPBD), one of districts/cities in East Java is categorized as prone against landslides is at Probolinggo District where 108 disaster occurred over 2021. Landslide disasters certainly have an impact on the environment and social aspects in vicinity. Therefore, this research aim to assess areas which has prone level against landslides in terms of social and environmental vulnerabilities through analysis of potential for landslide disasters in Probolinggo District. The research using weighting parameter methods in order to obtain the level of social and environmental vulnerability. It is divided into four categories namely low, medium, high, and very high. Maron sub-district become the largest area with very high level of social vulnerability about 33.7% of the area of one sub-district. Meanwhile, in area of environmental vulnerability which is dominated by very high levels of vulnerability is at Krucil sub-district about 88.5% of the area of one sub-district.

Landslide disaster risk analysis in Pacet District, Mojokerto Regency, East Java

Pacet District, Mojokerto Regency, East Java is one of the areas prone to landslides. Landslides in the area that occur can threaten lives and cause environmental damage and financial losses. This study aims to analyze the risk of landslides in Pacet District, Mojokerto Regency, East Java, and apply Early Warning System (EWS) technology based on the Internet of Things (IoT). This type of research is both qualitative and quantitative, the results of the study are based on the level of threat of landslides. Designing a landslide early warning tool is not easy to do because it has to be adapted to local situations and conditions. This program will be directed at technology transfer by implementing an Internet of Things (IoT)-based Early Warning System (EWS).