Landslide Early Warning System Research Articles

Rock landslide early warning system combining slope stability analysis, two-stage monitoring, and case-based reasoning: a case study

Rock landslide early warning system combining slope stability analysis, two-stage monitoring, and case-based reasoning: a case study

Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

Shallow landslides are among the natural threats causing death and damage. They are mostly triggered by rainfall in mountainous areas where precipitation used to be abundant. The amount of rainfall inducing this natural threat differs from one site to another based on the geographical characteristics of that area. In addition to the rainfall depth, the determination of soil water content in a specific zone has a major contribution to the landslide prediction and early warning systems. Rwanda being a country with hilly terrains, some areas are susceptible to both rainfall and soil water content inducing landslides. But an analytical study of the physical threshold determination of both rainfall and soil water content inducing landslides is lacking. Therefore, this experimental study is conducted to determine the rainfall and soil water content threshold that can be fed in to the landslide early warning system (LEWS) for alert messages using the Internet of Things (IoT) technology. Various experiments have been conducted for the real-time monitoring of slope failure using the toolset composed of a rain gauge, soil moisture sensors, and a rainfall simulating tool. The results obtained show that the threshold for landslide occurrence does not solely correlate with the total rainfall amount (or intensity) or soil moisture, but also influenced by internal (geological, morphological) and environmental factors. Among the sampled sites, the sites covered by forest indicated no sign of slope failure, whereas sites with crops could slip. The experiments revealed that for a specific site, the minimum duration to induce slope failure was 8 hours, 41 minutes with the rainfall intensity of 8 mm/hour, and the soil moisture was above 90% for deeper sensors. These values are used as thresholds for LEWS for that specific site to improve predictions.

Simulated or measured soil moisture: which one is adding more value to regional landslide early warning?

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWSs). However, it is still unclear which source of information – numerical models or in situ measurements – is of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019, using a physically based 1D soil moisture transfer model. A common parameterization set was defined for all sites, except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and receiver operating characteristic analysis (ROC) was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different model parameterizations, to various distances between simulation sites and landslides and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes, and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

Fiber Bragg Grating–Based Flume Test to Study the Initiation of Landslide-Debris Flows Induced by Concentrated Runoff

Abstract In order to explore the initiation of landslide-debris flow caused by concentrated runoff, a flume test has been performed on soil samples taken from a debris flow site in Wenchuan earthquake area. The fiber Bragg grating (FBG) technology was used to measure the strain distribution information in the slope mass during erosion. Several hydrological sensors for monitoring pore water pressure and soil moisture were used to investigate their correlation with internal strain field. The test results show that the fiber optic sensing technique exhibited high sensitivity and precision in monitoring the slope deformation. The development of pore water pressures and soil moistures showed reasonable consistency with the dynamics of an infiltration. According to the strain measurements, there were four evolution stages during the initiation of landslide-debris induced by runoff, i.e., the water absorption stage, the deformation stage, the shear zone formation stage, and the fluidization stage. The results provide an improved insight into the mechanism of debris flow initiation and indicate the enormous potential of the FBG sensing technology in establishing an effective early warning system for landslides and debris flows.

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems.

Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.

Prediction interval estimation of landslide displacement using adaptive chicken swarm optimization-tuned support vector machines

Accurate and reliable displacement prediction is vital for developing landslide early warning system, since displacement variations directly reveal the evolution of landslide. The past decade has seen the rapid development of displacement prediction technology. Most of the existing studies are based on deterministic point prediction models, with the goal of obtaining high-precision predictions. However, these pointwise predictions do not give any indication of credibility, which makes it difficult to make accurate decisions for subsequent landslide treatment. To address this problem, a prediction interval estimation method, instead of point prediction, for landslide displacement is proposed. In the methodology, double exponential smoothing is employed to deal with the nonlinear cumulative displacement, while two support vector machines are proposed to directly generate the displacement lower boundary and upper boundary. To obtain optimal model parameters, the adaptive chicken swarm optimization using chaotic mapping and adaptive inertia weight strategies is proposed to minimize a modified prediction interval-based objective function. A real-world landslide displacement data set is used to demonstrate the proposed method. Experimental results show that it is a promising tool for the construction of high-quality prediction intervals, which can inform the landslide treatment-related decision-making.

Negotiated participatory action research for multi-stakeholder implementation of early warning systems for landslides

The Dynaslope project of the Department of Science and Technology - Philippine Institute of Volcanology and Seismology (PHIVOLCS) operates an early warning system for deep-seated landslides (EWS-L) to increase resilience of communities at risk. To ensure the sustainability of the system and the empowerment of the partner communities, a people-centered EWS-L is optimal. A multi-stakeholder EWS-L (MEWS-L) that includes all relevant stakeholders in the community-based system has a better chance to strengthen risk governance.This paper attempted to establish a MEWS-L using the proposed negotiated participatory action research (NPAR) paradigm. The research is done in one local community catered by PHIVOLCS. The research consisted of problem analysis, action planning, and a preliminary reflection session. Negotiation principles were integrated to this process to ensure that there is a win-win situation among all stakeholders in the research.A concrete action plan was developed which included tasks related to infrastructure, organization, training, and communication needs in establishing the MEWS-L. These needs were connected to societal development and other DRR strategies as outcome indicators. Underlying problems were also uncovered: policy and planning needs, community commitment, project sustainability and political support. Commitments to the action plan determined in the planning stage were secured from the stakeholders to ensure future implementation of their MEWS-L.The study recommended the continuous monitoring, and evaluation of the action plan to ensure success of the system. The study has shown that the NPAR model can be used as a participatory framework in developing EWS-L in communities which involves different stakeholders with varying interests.

Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil wetness using an area under the curve (AUC) based on receiver operating characteristics (ROC) curve method. The results show that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rainfall events can also trigger landslides when the soil is wet. The role of soil wetness in the initiation is used to improve the performance of the conventional methods, and a ROC approach was used for the statistical comparison of different models. Further, the results indicated that the probabilistic threshold using rainfall severity and soil wetness outperformed the conventional approaches with AUC of 0.96, being the most sensitive and specific among the models considered. This result opens new promising perspectives for the development of an operational LEWS in the Idukki district based on a combination of rainfall and soil moisture data. Moreover, this work contributes to strengthen the advancing trend of hydro-meteorological thresholds based on soil moisture, which is gaining a growing attention in landslide studies and that, to date, was lacking evidences in monsoon regions.

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

Exploring ERA5 reanalysis potentialities for supporting landslide investigations: a test case from Campania Region (Southern Italy)

Recently, ECMWF has released a new generation of reanalysis, acknowledged as ERA5, able to deliver a comprehensive, free, and operative picture of the past weather, exploiting the data assimilation of historical observations from different sources (satellite, in situ, multiple variables) for both atmospheric and soil variables. Experiences concerning flooding issues suggest that ERA5 could support also landslide investigations. In this sense, a number of questions may be raised: (i) can ERA5 data be reliable in reproducing rainfall histories leading to a landslide event? (ii) can ERA5 soil moisture estimations be reliable proxies for antecedent slope wetness condition? (iii) can ERA5 be implemented in a landslide early warning system to improve the performances? This study tries addressing these questions referring to some historical Campanian events (Southern Italy) with a special focus on the fully investigated event occurred in Nocera Inferiore on March 4, 2005. The study shows the following: (i) ERA5 precipitation can appropriately reproduce actual rainfall histories leading to the events occurred in the last years; (ii) ERA5 soil moisture may act as proxy of slope wetness conditions; (iii) ERA5 data could be easily implemented in LEWSs in areas poorly covered by field monitoring networks.

Research on Submarine landslide monitoring and early warning system

Monitoring and early warning of submarine landslides could provide instant predictions for landslides, which is to avoid the destructive damage of submarine facilities such as pipelines and optical cable, etc effectively. However, researches on submarine landslide focus on numerical simulation and laboratory test, lacking support of in-situ observation data. This paper established the submarine landslide monitoring and early warning system by combining real-time monitoring data with web network platform and database technique. Based on the computational analysis of key monitoring parameters in the process of seabed deformation and sliding, the system has realized the accurate prediction and early warning of submarine landslides. The system has been applied to the submarine landslide monitoring in Zhoushan sea area, Zhejiang province, China, which has ensured the safety of offshore platforms and submarine projects in this area. The establishment of this system provides a new idea and method for submarine landslide warning.

Forecasting landslides using SIGMA model: a case study from Idukki, India

This study proposes a regional landslide early warning system for Idukki (India), using a decisional algorithm. The algorithm forecasts the possibility of occurrence of landslide by comparing the rainfall thresholds with the cumulated rainfall values. The region has suffered severe socio-economic setbacks during the disastrous landslides that happened in 2018 and 2019. Rainfall thresholds are defined for Idukki, using the total amount of precipitation cumulated at different time intervals ranging from 1 to 30 days. The first three-day cumulative values were used for evaluating the effect of short-term rainfall and the remaining days for the effect of long-term rainfall. The derived thresholds were calibrated using historical landslides and rainfall data from 2009 to 2017, optimized to reduce the false alarms and then validated using the 2018 data. The validation results show that the model is effectively predicting 79% of the landslides that happened in the region during 2018 and can be easily integrated with a rainfall forecasting system for the prediction of landslides. The model can be further improved with the availability of better spatial and temporal resolution of rainfall data and can be used as an effective tool for predicting the occurrence of landslides.

Design and Development of Real-time landslide early warning system through low cost soil and rainfall sensors

Development of cost effective early warning systems is of paramount importance for real time monitoring of natural hazards such as Landslides, Floods, Earthquake etc. The present research work discusses about the design and development of cost effective early warning system for monitoring shallow debri landslides. The warning system consist of field unit and remote network interface. The field system includes the rainfall, temperature, humidity, soil temperature, soil moisture and Tiltmeter as primary sensors for monitoring the landslide movements with an independent power supply of 50 W solar panel. The datasets that are received are stored in the local data logger with the help of microsd card. Real time monitoring of the datasets and analysis was made possible by storing the datasets in remote Database Management System. These datasets are transmitted via the Global System for Mobile communication module installed at the field unit. In order to convey the alerts during emergency time a simple visual message has been adopted for the current model. Level I, Level II and Level III, Level I being the threshold values are within the desired limit and actions are not necessary and Level III denotes threshold values have exceeded the set limit and warning are issued to local stake holders. A pilot study of the Early Warning System has been installed on site for quality checks of the sensor & data and the results were monitored. The model developed not only can address the early warning signs of mass movement but also can be calibrated to predict flood in major streams based on rainfall intensity.

Sistem Peringatan Dini Rawan Bencana Longsor Di Kota Ambon menggunakan IoT

Landslide disasters not only cause property losses but also have a multidimensional impact, for example people's psychology becomes disrupted, relocation of residential settlements and even disruption of investment as capital for economic development in Ambon City. This research aims to design a early warning system for landslide prone disastersin Ambon city and analysis using IoT.The results of the analysis use satellite landsat imagery as supporting spatial data with overlay indicators for landslide causes, including: rainfall, soil type, slope, population and land cover combined with placing IoT devices in landslide prone areas resulting in an early warning system for landslides. This IoT device uses a soil moisture sensor to read soil moisture and an MPU6050 accelerometer sensor to read soil movements. The results of the two sensors if they meet the criteria for landslide prone will be processed and sent as a notification to the smartphone. This research produces a landslide detection system starting from the analysis of landslide hazard maps to using sensors to detect landslide symptoms and then sending notification as a danger sign.

Using Field-Based Monitoring to Enhance the Performance of Rainfall Thresholds for Landslide Warning

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area.

A Fast Deploying Monitoring and Real-Time Early Warning System for the Baige Landslide in Tibet, China.

Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by combining the Kalman filtering (KF), fast Fourier transform (FFT), and support vector machine (SVM) methods. We also designed a fast deploying monitoring system (FDMS) to monitor the displacement of landslides for real-time prediction. The FDMS can be quickly deployed compared to the existing system. This system also has high robustness due to the usage of the ad-hoc technique. The principle of this method is to extract the precursory features of the landslide from the surface displacement data obtained by the FDMS and, then, to train the KF-FFT-SVM model to make a prediction based on these precursory features. We applied this fast monitoring and real-time early warning system to the Baige landslide, Tibet, China. The results showed that the KF-FFT-SVM model was able to provide real-time early warning for the Baige landslide with high accuracy.

Landslide early warning, case studies from Southwest China

Landslides are one of the commonly natural disasters triggered by rainfalls, earthquakes, and human activities, which cause fatalities, damage to properties, and economic losses in the world. Early warning method may predict landslide occurrence and reduce the risk. In this paper, we propose a self-developed site-specific landslide early warning system (LEWS), which has been progressively designed and accomplished over the past decade in Southwest China. The warning model for the prediction of slope instability is focused on multiple thresholds, including deformation rate, rate increment and tangential angle related to surface displacement measurement. The debris-flow initiation is determined by critical rainfall thresholds integrated with the topographic and geological conditions at a catchment scale. The recent Xingyi rockslide, Baige debris slide and debris flows in Zoumaling catchment are selected to explain the specific process of landslide early warning in a real-time operating system. The performance of the presented warning system is evaluated by comparison with the inverse-velocity model (INV) and gradient model (SLO) in the prediction of slope failure time. The Receiver Operator Characteristic (ROC) analysis is used to prove its feasibility and reliability in the debris-flow initiation prediction. The presented warning model and system can be applied to other regions in landslide early warning and to be useful to mitigate landslide losses and damages.

Estimation of rainfall threshold for the early warning of shallow landslides along National Highway-10 in Darjeeling Himalayas

The Darjeeling Himalayan region receives heavy rainfall during the monsoon and as a result is known for having many shallow landslides. These monsoon-induced landslides cause massive disruption to traffic on the National Highway-10 (NH-10), which serves as a life-line for the area. Developing an early warning system for landslide occurrence can contribute to minimize the risk. In the present study, an attempt is made to estimate the rainfall threshold for initiation of landslides which may help in developing an early warning system for rainfall-induced shallow landslides. To work out the threshold for rainfall in triggering landslides, a rainfall intensity-duration model has been developed for the study area. Daily rainfall data were obtained from the India Meteorological Department (IMD) rain gauge stations and adjacent tea gardens along NH-10, whereas landslide event-related information was obtained from the Public Works Department (PWD), newspapers, and earlier publications. The study was carried out separately on four lithostratigraphic units along NH-10 between 2011 and 2018. Over this time, 288 rainfall events were reported which are responsible behind triggering 681 landslides along this road corridor. The overall threshold for the entire NH-10 has been estimated as I = (20.10 ± 1.84)D(−0.45 ± 0.05), where I is the rainfall intensity (mm/d) and D is the event duration (d). The result shows that the area with a slate lithology is more susceptible to landslides than in areas with other rock types. Along the NH-10 corridor in general, over a cumulative rainfall event of 3 or 5 days before the failure, the daily rainfall on the final day is effective in triggering about 17% and 8% of landslides, respectively.

Effect of basin morphometric parameters on physically-based rainfall thresholds for shallow landslides

The morphometric characteristics of a basin have a significant effect on landslide occurrence and can provide valuable information related to rainfall-induced shallow landslides. Basin morphometric parameters (e.g. average slope, relief and Melton ratio) can be correlated to landslide triggering factors such as rainfall intensity and duration. In this study, the physically-based model TRIGRS was used to determine rainfall intensity and duration (I-D) thresholds for shallow landslides in 93 small basins in the Colombian Andes. The results show highly variable thresholds, even for basins in the same region that are very close together. Different morphometric parameters were calculated for each basin and their effect on the threshold (power-law curves) characteristics was analysed. The morphometric parameters with greater effect on the threshold position (in the I-D graph) are mainly related to the slope gradient, but other elements (e.g. soil properties and basin area), that are important to consider in a physically-based methodology for defining rainfall thresholds, are also described. This analysis is useful for a better understanding of the factors affecting the threshold variability. It could be significant in the subsequent validation of rainfall thresholds defined using this methodology and its implementation in a landslide early warning system in areas for which landslide inventories are not available.