Landslide Warning Research Articles

Design and Build a Website-Based Landslide Early Warning System

An early warning system is very important to implement to minimize losses received when a disaster occurs, one of which is a landslide disaster. An early warning system for landslides is carried out so that people can find out the signs of a disaster that can be accessed anywhere, so they can prepare themselves. For this reason, a tool is needed that can detect and provide early warning of landslides to the public which can be accessed through the website. In this study, a website-based landslide detector has been designed using a soil moisture sensor to measure it and the MPU6050 sensor to measure the slope of the soil. Sensor data will be displayed on the LCD and sent to the database and can be monitored through the website. The avalanche detector made is powered by a 15 V DC battery to provide 5V output to the NodeMCU ESP8266. Soil moisture sensor, MPU6050, LCD I2C, LED, and relay work at 5V. A Relay is used to turn on the siren at 12V voltage. Based on our research, the soil moisture measurement value between the sensor and the comparison device is around 1.55% and the soil slope measurement value between the sensor and measuring tool is around 0.14%, these two factors influence each other. The measurement error is still fairly normal according to the soil moisture sensor datasheet and MPU6050, which are ±3% together

Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation

Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.

Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data

Abstract. This work aims to generate and evaluate regional rainfall thresholds obtained from a combination of high-resolution gridded rainfall data, developed by the National Service of Meteorology and Hydrology of Peru, and information from observed shallow landslide events. The landslide data were associated with rainfall data, determining triggering and non-triggering rainfall events with rainfall properties from which rainfall thresholds are determined. The validation of the performance of the thresholds is carried out with events that occurred during 2020 and focuses on evaluating the operability of these thresholds in landslide warning systems in Peru. The thresholds are determined for 11 rainfall regions. The method of determining the thresholds is based on an empirical–statistical approach, and the predictive performance of the thresholds is evaluated with true skill statistics. The best predictive performance is the mean daily intensity–duration (Imean−D) threshold curve, followed by accumulated rainfall E. This work is the first estimation of regional thresholds on a country scale to better understand landslides in Peru, and the results obtained reveal the potential of using thresholds in the monitoring and forecasting of shallow landslides caused by intense rainfall and in supporting the actions of disaster risk management.

Community-Level, Participatory Co-Design for Landslide Warning with Implications for Climate Services

Inclusive, participatory governance is a key enabler of effective responses to natural hazard risks exacerbated by climate change. This paper describes a community-level co-design process among academic, state, and federal scientists and the community of Sitka, Alaska to develop a novel landslide warning system for this small coastal town. The decentralized system features an online dashboard which displays current and forecast risk levels to help residents make their own risk management decisions. The system and associated risk communications are informed by new geoscience, social, and information science generated during the course of the project. This case study focuses on our project team’s activities and addresses questions including: what activities did the project team conduct, what did these activities intend to accomplish, and did these activities accomplish what they intended? The paper describes the co-design process, the associated changes in system design and research activities, and formal and informal evaluations of the system and process. Overall, the co-design process appears to have generated a warning system the Sitka community finds valuable, helped to align system design with local knowledge and community values, significantly modified the scientists’ research agendas, and helped navigate sensitivities such as the effect of landslide exposure maps on property values. Other communities in SE Alaska are now adopting this engagement approach. The paper concludes with broader implications for the role of community-level, participatory co-design and risk governance for climate services.

Experimental study on disaster mechanism of completely weathered granite landslide induced by extreme rainfall

BackgroundWith the increased frequency of extreme weather, landslides induced by extremely heavy rainfall pose a major threat to human lives and property safety. In July 2020, a landslide occurred in the strongly weathered Laoshan Scenic Spot in Qingdao, China, and a short period of heavy rainfall was the main factor inducing the landslide.MethodBased on the similarity criterion, three groups of laboratory model tests under different extreme rainfall conditions were conducted using a large landslide model test box. The rainfall infiltration responses under different rainfall intensities, deformation processes, and failure modes of the slope were analyzed. The inducing mechanism and rainfall disaster process of granite landslides induced by extreme rainfall in strongly weathered areas were summarized.ResultThe results showed that (1) a completely weathered granite landslide induced by rainfall had four stages, i.e., infiltration erosion, surface deformation, damage deepening, and overall instability, and the landslide was characterized by "sheet slip". (2) With greater rainfall intensities, the rainfall infiltration rate was higher, the changes in soil pressure, pore water pressure, water content, and matrix suction were faster, and the hysteresis effect was weaker. (3) A certain spatial distribution pattern was observed between slope deformation/damage and rainfall infiltration, and the research results could provide references for landslide warning and treatment in strongly weathered granite areas.

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

Combining soil macropore flow with formation mechanism to the development of shallow landslide warning threshold in South China

Because of the physical character of soil from granite weathering and the typhoon rains in South China, the model for early warnings of existing shallow landslides cannot be well applied in that area. This study developed a new shallow landslide model based on the effect of soil macropores for determining the rainfall threshold to post an early warning of the possibility of a shallow landslide in South China. We studied the hydrological effects of macropore flow and proposed a mechanistic model of the formation of shallow landslides by introducing the macropore coefficient of granite residual soil. The rainfall threshold of each slope unit was calculated by combining the hydrological model with the proposed shallow landslide model. Lastly, we tested the calculated result in the Maguihe watershed, Guangdong province, South China, which experienced a group of massive shallow landslides on 21 September 2010. The study results showed that the macropores of vegetation roots had a significant effect by increasing the permeability of granite residual soil. Coniferous forest land has the highest initial infiltration rate and stable infiltration rate, followed by shrub forest land, and then bare land. Statistical verification showed that the accurate prediction rate of the proposed model was 80.65%, which is adequate for early warning of shallow landslides in South China. We discuss the application conditions and parameter calibrations of the proposed model, and offer recommendations for future research.

Identification and deformation analysis of potential landslides after the Jiuzhaigou earthquake by SBAS-InSAR.

A World Natural Heritage Site, Jiuzhaigou, is the first nature reserve in China whose primary purpose is to protect natural scenery. On August 8, 2017, a Ms 7.0 earthquake caused many unstable slopes in Jiuzhaigou, Sichuan Province, China. In the extreme storm conditions that follow, the unstable slopes tend to develop into potential landslides, which can cause many casualties and property losses in scenic areas. Sentinel-1A ascending orbit data were obtained in this paper to establish a SAR database. The large-scale deformation rate map of the study area was obtained using a small baseline set InSAR technology. The potential landslides in the deformation area are preliminarily confirmed with remote sensing interpretation. The field verification is further carried out by studying the deformation information of the characteristic points on the potential landslides. The results show that 13 deformation zones were preliminarily identified, and three typical deformation zones were selected for coupling verification and identified as potential landslides. At the same time, further analysis shows that the four potential landslides have been in continuous linear deformation for a long time since the earthquake, posing a severe threat to the safety of local people's lives and property. The research results provide a reference for the early identification and warning of potential landslides in earthquake-prone regions.

Elevation Angles, Soil Textures, Soil Settlements and WaterHolding Capacity on Landslides: An Experimental Case Study in the Province of Iloilo, Philippines

The soil textures, soil settlements, soil water-holding capacity, and elevation angles are parameters for landslides in the Province of Iloilo. They are usually triggered during heavy rainstorms, causing severe property damage and sometimes causing casualties. This experimental study aims to determine how these parameters are factors for landslides and flood monitoring and uses three methods to provide ease and continuity of measurements. The Fourier transforms infrared (FTIR)spectroscopy analysis for soil textures, the Imhoff cone for the settling and water-holding capacity of the soil, and an improvised apparatus to test how the slopes affect the transportation of soil materials. FTIR Soil analysis reveals that contents of clay and organic matter directly affect soil water-holding capacity due to the larger surface area. The smaller particle sizes, such as clay, have a larger surface area leading to a higher water-holding ability. A landslide-prone zone has a lesser settling time except for the sand that settles fastest due to larger masses. An angle of 20 degrees has more mass movement too much higher slopes like 55 and 85 degrees. The increase in height or angle of the elevation decreases stability, leading to less mass movement. Less mass movement leads to the conclusion that there are rare landslides in the very high slope range due to fewer vibrations or soil movements caused by human activities and being compact or stable. This study is crucial for forecasting and preventing geohydrological processes and developing better landslide warning strategies to mitigate risks and reduce socioeconomic damage.

Monitoring Seasonal Movement Characteristics of the Landslide Based on Time-Series InSAR Technology: The Cheyiping Landslide Case Study, China

Landslides are one of the extremely high-incidence and serious-loss geological disasters in the world, and the early monitoring and warning of landslides are of great importance. The Cheyiping landslide, located in western Yunnan Province, China, added many cracks and dislocations to the surface of the slope due to the severe seasonal rainfall and rise of the water level, which seriously threaten the safety of residents and roads located on the body and foot of the slope. To investigate the movement of the landslide, this paper used Sentinel-1A SAR data processed by time-series interferometric synthetic aperture radar (InSAR) technology to monitor the long-time surface deformation. The landslide boundary was defined, then the spatial distribution of landslide surface deformation from 5 January 2018 to 27 December 2021 was obtained. According to the monthly rainfall data and the temporal deformation results, the movement of the landslide was highly correlated with seasonal rainfall, and the Cheyiping landslide underwent seasonal sectional accelerated deformation. Moreover, the water level change of the Lancang River caused by the water storage of the hydropower station and seasonal rainfall accelerates the deformation of the landslide. This case study contributes to the interpretation of the slow deformation mechanism of the Cheyiping landslide and early hazard warning.

Rainfall Threshold for Landslide Warning in the Southern Thailand – An Integrated Landslide Susceptibility Map with Rainfall Event – Duration Threshold

Rainfall Threshold for Landslide Warning in the Southern Thailand – An Integrated Landslide Susceptibility Map with Rainfall Event – Duration Threshold

Location-Aware IoT-Enabled Wireless Sensor Networks for Landslide Early Warning

Wireless Sensor Networks (WSNs) represent an interesting technology for designing early warning systems for landslides as they can ensure real-time and continuous monitoring. Through accurate localization techniques, changes in the position of installed nodes can be detected even during the early stage of field instability. This is through an accurate detection of nodes position changes independently from systematic deviations resulting from outdoor environmental conditions. In this study, we propose an accurate measurement system for distance measurement between wireless sensor nodes based on an ultra-wideband (UWB) localization method. In particular, distance measurements at different real weather conditions were performed to identify the impact of weather changes on distance measurement deviations. A prototype for a landslide warning system has been developed realizing a localization accuracy of 98%.

Failure mode of rainfall-induced landslide of granite residual soil, southeastern Guangxi Province, China

Abstract. Granite residual soil landslides are widely distributed in the southeast of Guangxi, China. They pose threats to local communities, economic development and ecological restoration. To understand the failure mode, the landslide can provide a scientific basis for early warning and prevention. In this study, we conducted artificial flume model tests to investigate the failure mode of granite residual soil landslide. The macroscopic phenomena of landslides were observed and summarized. The response and variations of soil moisture content and pore water pressure were analyzed. And the discrepancies in landslide initiation were explored. The results had three aspects: (1) the response of volume moisture content was not synchronized with that of pore water pressure. Their variations were influenced by initial dry density, slope angle and rainfall intensity. The fluctuation of pore water pressure depended on soil mechanical behavior and its diffusion. (2) The differences in the formation process of granite residual soil landslides included the initiation time and mode. The starting time of landslide was delayed with increasing initial dry density and slope angle but shortened with increasing rainfall intensity. The failure mode could be changed from a sudden type to a progressive type due to the increase in initial dry density. (3) There are five stages in the landslide mobilization as follows: rain infiltration and crack generation, soil slide at the slope toe, occurrence of surface runoff and soil erosion, formation of steep-free surface, and soil slide at the upper slope. This research can provide valuable reference for the prevention and early warning of granite residual soil landslide in southeastern Guangxi.

A new early warning Criterion for assessing landslide risk

A large number of engineering case studies have shown that the traditional early warning criteria, which evolved on the basis of displacement as a single piece of information, have many limitations in practical engineering. The displacement speed ratio (DSR) cannot determine the development trend of landslides due to the influence of periodic external environmental factors. Moreover, when landslides occur, the early warning system will have a false alarm due to the stepwise giant rise of landslides. To solve this problem, this paper proposes a new landslide warning criterion, the trend speed ratio (TSR), and also fuses TSR and DSR into a dual speed ratio method (DSRM) for judging landslide risk changes. We assess these methods by applying DSRM and DSR to 10 landslide cases, respectively. The results show that when TSR is greater than 2.0, the probability of landslide damage is high and when TSR tends to decrease, the landslide tends to be stable. For landslides that rise sharply in steps but are not damaged, DSR has a high false alarm rate, while DSRM can effectively reduce the false alarm rate. In terms of warning applicability, DSR can be applied in only half of the ten landslide cases studied, while DSRM is significantly more applicable. In addition, compared with the traditional method, the new method can determine the direction of landslide development and assess the risk of step-up landslides, providing new technical support for engineers engaged in landslide warning and control.

Novel Intelligent Approach for the Early Warning of Rainfall-Type Landslides Based on the BRB Model

This work attempts to apply belief rule-based (BRB) model in information fusion method to landslides, to improve the accuracy and efficiency for early warning of landslides. Taking a typical rainfall-type landslide as the experimental area, the monitoring results find that the surface displacement is the most sensitive monitoring data. It is determined that the monitoring data of surface displacement change rate and rainfall intensity could be used as the input parameter of the BRB model. An initial BRB model is established by setting up the rule base for discriminating warning levels. The data from three monitoring points are collected for the optimization of the initial BRB model, and verification of the optimized BRB models. Results shows the optimized BRB model can accurately describe the nonlinear relationship between the selected monitoring data and the warning level, which provides an intelligent method for landslide prevention and has a strong application prospect.

A semantic information-driven stepwise landslide displacement prediction model.

Landslide prediction is critical for the early warning of a landslide occurrence. Existing stepwise landslide displacement prediction methods are mostly data-driven approaches. However, these models are vulnerable to overfitting, and the low-dimensional numerical features with high numerical volatility prevent them from precisely quantifying the rapid increase in daily displacement in the acceleration phase. Therefore, we propose a semantic information-driven stepwise landslide displacement prediction model comprising an identifier in the displacement phase and a predictor in the acceleration phase. First, the raw landslide monitoring data are converted into text-based semantic information and the semantic features are fused. Subsequently, based on the daily displacement and velocity, we propose a sliding window phase division algorithm to divide the stepwise landslide phase into stationary and acceleration phases. Finally, the landslide displacement phase is identified, and the displacement during the acceleration phase is predicted. The experimental results of the model on the Xinpu and Qingshi landslides in Chongqing, China, show that the proposed model exploits the derived semantic information to identify the landslide acceleration phase qualitatively, and predict the daily displacement of the acceleration phase quantitatively. The proposed model provides a valuable reference for the early warning of stepwise landslides.

A new forecasting method for failure time of creep landslide based on nonlinear creep behavior and new pre-warning criterion

The forecast of failure time of unstable slope and the definition of early warning threshold are very important for preventing landslide disaster and reducing its losses. Based on the monitoring curve of unstable slope deformation varying with time, the mathematical models are used to accurately describe the nonlinear creep behavior in the initial creep stage and the unstable creep stage of unstable slope and a forecasting method for creep landslide is proposed. In addition, this study examines an improved tangential angle criterion obtained by a new forecasting method. The results show that the initial creep stage of unstable slope can not be neglected for forecasting the failure time. The initial creep velocity v0 and the viscoelastic hysteresis coefficientξof the slope are determined at initial creep stage, which together control the creep process of the unstable slope. Moreover, in the secondary creep stage, there is an inverse relationship between the velocity and the critical tangential angle. According to the average velocity of the secondary creep stage, the early warning criterion of the landslide tangential angle is proposed. Combined with the forecast parameters of the unstable slope, comparative analysis of the critical tangential angle warning criterion and an improved tangential angle criterion, the early warning and forecasting system of landslide with creep characteristic is established. This system is applied to the early warning and forecasting analysis of reported soil and rock landslides cases with creep characteristics. The forecasting effect and applicability of the new method are studied in order to make it a better supplement to the early warning strategy of landslide.

Multi-sensor data fusion technology for the early landslide warning system

Multi-sensor data fusion technology for the early landslide warning system

Landslide rainfall threshold for landslide warning in Northern Thailand

Northern Thailand is a hotspot for landslides. Rainfall-triggered landslides in this region have caused much suffering and many fatalities. In this work, a landslide-triggering rainfall threshold for Northern Thailand is proposed based on rainfall data relating to 48 triggering rainfall events that caused 59 landslides in the study area. To account for different mechanism of landslide formation, the threshold was portioned into two parts for different duration of rainfall events. A split point of 3 days was chosen as a separator for portioning the threshold to be 1) a threshold for rainfall events of duration no longer than 3 days and 2) a threshold for rainfall events of duration longer than 3 days. The threshold also required a suitable variable for antecedent rainfalls which was found to be cumulative rainfall over 25-day period (CR 25) of 140 mm. Therefore, the thresholds combining cumulative rainfall with rainfall event - duration (CED) were established by incorporating the CR25 of 140 mm into the traditional ED threshold. This is the first attempt to incorporate the difference mechanism of landslide formation by dividing the CED threshold to two portions for difference rainfall duration. The introduced threshold shows positive sign of the prediction, particularly in term of false alarm rate, false alarm ratio, and critical success index. The introduced threshold will be useful for landslide warning system in the study area.

Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning

Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning