Landslide Early Warning System Research Articles

Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Sikkim, one of the Northeastern states of India, is a famous tourism spot in the Himalayas with dynamic population density. This mountainous area receives heavy rainfall and is well known for frequent shallow landslides, especially, Chandmari, which is a village, situated in Gangtok area in East Sikkim. Even though it is well known that rainfall and landslides are correlated, Sikkim lacks a well-established landslide early warning system. Such a system is important in this region because it is one of the highest landslide-prone areas in India. The current research attempts to establish rainfall thresholds as part of developing an efficient landslide early warning system for this region. The rainfall thresholds for landslides are derived based on the daily rainfall data available from India Meteorological Department (IMD) for six stations in Sikkim. Analysis of daily rainfall data and landslide events in this area between the year 1990 and 2017 is performed. An intensity–duration (I–D)-based regional rainfall threshold is derived as I = 43.26 D−0.78 (I = rainfall intensity in mm/day and D = duration in days) for the rainfall-triggered landslides in Sikkim region and a local threshold of I = 100 D−.92 was developed for the Gangtok area. Furthermore, the influence of antecedent rainfall in landslide initiation is explored by considering the daily, 3-day, 5-day, 7-day, and 20-day cumulative rainfall values associated with landslides. The proposed threshold equations and study of the effect of antecedent rainfall on landslides are intended to aid in enhancing the real-time landslide early warning system (R-LEWS) being developed for Sikkim.

Integrated experiments on field monitoring and hydro-mechanical modeling for determination of a triggering threshold of rainfall-induced shallow landslides. A case study in Ren River catchment, China

Understanding the hydrological mechanism of rainfall-induced shallow landslides is critical to make reliable predictive assessments and to reduce risks of economic loss and fatalities. An integrated approach was identified by combining the field-based monitored hydrological data with a hydro-mechanical analysis, which can be utilized to better understand seasonal hydrological features in shallow soils and the corresponding triggering mechanism of rainfall induced shallow landslides, with the aim of setting up an improved landslide early warning system. A hydrological monitoring system on a representative slope that was experiencing frequent local shallow failures in the Ren River catchment was constructed to provide available seasonal hydrological features with regard to time trends in volumetric water content and pore water pressure in shallow soils. A transition of vertical downward flow from unsaturated to near-saturated conditions has been fully detected since the significant saturation degree and associated pore pressure varies with vertical depths. Consequently, the triggering mechanism of shallow landslides should be associated with the combined effects of dissipation of matrix suction in the upper unsaturated zone and increased positive pore pressure due to the generation of a temporal perched water table in the deeper saturated zone. This process was confirmed through the field evidence and a back analysis on the long-term hydrological evolution of slope soil using a calibrated one-dimensional hydrological model. Antecedent hydrological conditions were also found to have an effect on rainfall intensity–duration thresholds in the landslide early warning system based on a hydro-mechanical analysis, with the aim of improving the accuracy of the model’s predictive ability. The results indicated that this research promoted the effectiveness of a landslide early warning system through a hydro-mechanical analysis based on capturing the real hydrological features of a landslide.

Ontology-based discovery of time-series data sources for landslide early warning system

Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources.

Successive landsliding and damming of the Jinsha River in eastern Tibet, China: prime investigation, early warning, and emergency response

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events.

Implementation and configuration of GB-SAR for landslide monitoring: case study in Minami-Aso, Kumamoto

ABSTRACTIn this paper, the applicability of ground-based synthetic aperture radar (GB-SAR) as an early warning system for landslide monitoring is discussed. The effectiveness of the differential interferometric SAR (DInSAR) technique used in GB-SAR depends strongly on the geography of the monitored location. Therefore, an assessment of the system compatibility to select the most appropriate remote monitoring method is essential prior to any hardware implementation. In the preliminary part of this study, a 3D model was created using a LiDAR survey, and proposed locations for GB-SAR installation were examined. A 3D simulation was carried out to estimate the illumination from each of the proposed GB-SAR locations. The proposed model increased the efficiency of the GB-SAR positioning by minimising installation cost and time. Hardware configuration parameters, such as platform height, maximum range, and the direction and view angle of the radar line of sight were estimated by considering the optimum reflected power and ground illumination. Unlike on flat terrain, deployment of GB-SAR in a mountainous area is challenging because of surface anomalies and continuous changes in meteorological parameters, such as atmospheric temperature, pressure and relative humidity. In this study, the experimental site was located 3 km from the Aso volcano, and the weather conditions in the Aso caldera became a critical factor in accurately estimating the interferometric phase. The presence of atmospheric artefacts also compromises the applicability of the classical DInSAR technique. Here, we minimised the atmospheric phase screen by estimating the optimum data acquisition interval from GB-SAR monitoring under extreme weather conditions. The developed methodologies were then used to design a new landslide early warning system that measures real-time displacement over an area of 1 km2 within 10 s of scanning. This fully automatic monitoring system updates every 15 min and presents displacement information in a 3D interface. The system we have developed has been deployed for continuous monitoring of the mountainous environment of a road reconstruction site in Minami-Aso, Kumamoto, Japan where a large-scale landslide was triggered following the Kumamoto earthquake in 2016.

Rainfall–landslide early warning system (RLEWS) using TRMM precipitation estimates

<span lang="EN-MY">This paper presents Rainfall–Landslide Early Warning System (RLEWS) using Tropical Rainfall Measuring Mission (TRMM) precipitation estimates to notify the warning level for the possibility of landslide occurrences in Ulu Kelang, Selangor. In this study, RLEWS is developed to monitor the possibility of rainfall-induced landslide occurrences by comparing real time TRMM rainfall data with a landslide rainfall threshold. The landslide rainfall threshold is constructed by using the accumulated rainfall-accumulated rainfall (E-E) diagram method. The warning levels of rainfall threshold are classified into three levels; high, moderate and low. The analysis and notification are updating every 24 hours to provide the initial potential landslide information signal. The rainfall threshold analysis was able to detect the early signal of initial potential landslide occurrences. The aims of this study are to develop a low-cost, sustainable early warning system and web base application to send notification and awareness for residential areas in Ulu Kelang, Selangor.</span>

Landslide early warning system based on arduino with soil movement and humidity sensors

In this paper, a landslide monitoring system was built to detect the movement and humidity of the soil that generally causes landslides. The soil movement sensors utilizes a sliding potentiometer that converts distances into stresses and a humidity sensors. Data from sensors processed using a 10 bit Analog to Digital Converter (ADC) on the ATmega8535 microcontroller. The value of soil movement and humidity then sent digitally via serial USB communication protocol. The soil movement and humidity is displayed through the website interface in the form of graphic data and can be downloaded in the form of excel files. The results obtained through the simulation that the system is able to measure the movement of soil, soil moisture, and provide early warning through sirens. The system has a 1 mm resolution specification with a range of motion measurements with a linear correlation of 0.998. The system can also recognize sudden ground movements as an indication of landslides.

Real-time Landslide Monitoring, Detection and Early Warning System for Tangni Landslide

India has been found as the hot spot of landslides and related disaster on the global map. It is affecting almost all hilly/mountain areas especially the Himalayan region. The seriousness and effect of disasters are gradually increasing both in terms of magnitude and frequency. It is causing extensive loss of human life and property and affecting sternly development in hilly areas. Thus, landslide disaster mitigation is one of the priorities for India. To reduce the risk due to such disasters, a low-cost, energy efficient and reliable Landslide Early Warning System (LEWS) for Himalayan landslides is proposed in this paper. It is an Internet of Things (IoT) protocols based landslide monitoring system consisting of a Wireless Sensor Network (WSN), data acquisition and analysis unit. Use of real-time streaming software and lightweight communication protocols is proposed to transmit the data to Edge Server and Cloud Server. For data analysis, the use of Machine Learning (ML) is proposed to improve the scope and quality of automated warnings.

Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh

This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (>250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

Characterizing the complexity of microseismic signals at slow-moving clay-rich debris slides: the Super-Sauze (southeastern France) and Pechgraben (Upper Austria) case studies

Abstract. Soil and debris slides are prone to rapid and dramatic reactivation. Deformation within the instability is accommodated by sliding, whereby weak seismic energies are released through material deformation. Thus, passive microseismic monitoring provides information that relates to the slope dynamics. In this study, passive microseismic data acquired at Super-Sauze (southeastern France) and Pechgraben (Upper Austria) slow-moving clay-rich debris slides (“clayey landslides”) are investigated. Observations are benchmarked against previous similar case studies to provide a comprehensive and homogenized typology of microseismic signals at clayey landslides. A thorough knowledge of the various microseismic signals generated by slope deformation is crucial for the future development of automatic detection systems to be implemented in landslide early-warning systems. Detected signals range from short-duration (< 2 s) quake-like signals to a wide variety of longer-duration tremor-like radiations (> 2 s – several min). The complexity of seismic velocity structures, the low quantity and low quality of available signal onsets and non-optimal seismic network geometry severely impedes the source location procedure; thus, rendering source processes characterization challenging. Therefore, we constrain sources' locations using the prominent waveform amplitude attenuation pattern characteristic of near-source area (< about 50 m) landslide-induced microseismic events. A local magnitude scale for clayey landslides (ML−LS) is empirically calibrated using calibration shots and hammer blow data. The derived ML−LS returns daily landslide-induced microseismicity rates that positively correlate with higher average daily displacement rates. However, high temporal and spatial resolution analyses of the landslide dynamics and hydrology are required to better decipher the potential relations linking landslide-induced microseismic signals to landslide deformation.

Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath

Abstract. Landslide hazard motivates the need for a deeper understanding of the events that occur before, during, and after catastrophic slope failures. Due to the destructive nature of such events, in situ observation is often difficult or impossible. Here, we use data from a network of 58 seismic stations to characterise a large landslide at the Askja caldera, Iceland, on 21 July 2014. High data quality and extensive network coverage allow us to analyse both long- and short-period signals associated with the landslide, and thereby obtain information about its triggering, initiation, timing, and propagation. At long periods, a landslide force history inversion shows that the Askja landslide was a single, large event starting at the SE corner of the caldera lake at 23:24:05 UTC and propagating to the NW in the following 2 min. The bulk sliding mass was 7–16 × 1010 kg, equivalent to a collapsed volume of 35–80 × 106 m3. The sliding mass was displaced downslope by 1260 ± 250 m. At short periods, a seismic tremor was observed for 30 min before the landslide. The tremor is approximately harmonic with a fundamental frequency of 2.3 Hz and shows time-dependent changes of its frequency content. We attribute the seismic tremor to stick-slip motion along the landslide failure plane. Accelerating motion leading up to the catastrophic slope failure culminated in an aseismic quiescent period for 2 min before the landslide. We propose that precursory seismic signals may be useful in landslide early-warning systems. The 8 h after the main landslide failure are characterised by smaller slope failures originating from the destabilised caldera wall decaying in frequency and magnitude. We introduce the term “afterslides” for this subsequent, declining slope activity after a large landslide.

Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis

The development of early warning systems for landslide hazards has long been a challenge because the accuracy of such systems is limited by both the complicated underlying mechanisms of landslides and the lack of in situ data. In this study, we implemented a multivariate threshold criterion that integrates in situ monitoring data and data from unsaturated hydro-mechanical analyses as an early warning system for rainfall-induced landslides in the Wenchuan earthquake region of China. The results indicate that rainfall intensity is closely correlated with the probability of landslide occurrence. Variations in matric suction and suction stress were obtained from in situ measurements and used to quantify the soil water retention curve, which presented clear hysteresis characteristics. The impacts of rainfall infiltration on slope failure in post-earthquake landslide areas under transient rainfall conditions were quantified by hydro-mechanical modelling theories. Variations in the suction stress of unsaturated soil were used to calculate the safety factor. The influence of hydrological hysteresis processes on the slope failure mechanism was analysed. Multivariate threshold criteria that include the intensity-probability (I-P) threshold, soil moisture and matric suction based on in situ big data and unsaturated slope stability analysis benchmarks are proposed for use in an early warning system for rainfall-induced landslides.

Low-Cost Vibration Chamber for Landslide Sensory and Alarm System

<p>Many previous research works published in the open literature aimed at designing a system that could detect landslide in early stage before the landslide becomes catastrophic. This paper presents a work-in-progress landslide early warning system for Malaysian environment. The aim of this paper is to develop the most effecienctly reliable cost-effective system in which slight earth movements are monitored continuously. The challenge this work aims at is to work with a low budget system that produce efficient performance. Hence, the material used is of-the-shelf. Early design optimization result of the vibration sensor used is quite promising detecting the slightest faint tremors, which are amplified using the best vibration chamber available. It is shown that the choice of proper pipe length and diameter dimensions in combination to a gravel to exaggerate the produced higher sensitivity level of 5dB. Furthermore, both systematic and random vibration tests produced similar results.</p>

An acoustic emission landslide early warning system for communities in low-income and middle-income countries

Early warning systems for slope instability are needed to alert users of accelerating slope deformation behaviour, enable evacuation of vulnerable people, and conduct timely repair and maintenance of critical infrastructure. Communities exposed to landslide risk in low- and middle-income countries seldom currently instrument and monitor slopes to provide a warning of instability because existing techniques are complex and prohibitively expensive. Research and field trials have demonstrated conclusively that acoustic emission (AE) monitoring can be an effective approach to detect accelerating slope movements and to subsequently communicate warnings to users. The objective of this study was to develop and assess a simple, robust, low-cost AE monitoring system to warn of incipient landslides, which can be widely deployed and operated by communities globally to help protect vulnerable people. This paper describes a novel AE measurement sensor that has been designed and developed with the cost constrained to a few hundred dollars (US). Results are presented from physical model experiments that demonstrate performance of the AE system in measuring accelerating deformation behaviour, with quantifiable relationships between AE and displacement rates. Exceedance of a pre-determined trigger level of AE can be used to communicate an alarm to users in order to alert them of a slope failure. Use of this EWS approach by communities worldwide would reduce the number of fatalities caused by landslides.

Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

The role of universities in Indonesia multi-hazard early warning system

Indonesia is a disaster-prone region facing multiple hazards. The impact has cost human casualties, injuries, damages to assets, environment and regional development. In order to protect the people and assets, the government has increased the capacity to face and anticipate disaster which is through a multi-hazard early warning system (MHEWS). The academic sector in Indonesia, especially prominent universities such as Institut Teknologi Bandung and Universitas Gajah Mada has contributed positively to the development of MHEWS and the Land Slide Early Warning System. This research aims to examine the role of universities in Indonesia related to the establishment of an MHEWS in Indonesia by answering what are the roles and the challenges. The research methodology is through a desk study and document review of government and organizations reports using qualitative and descriptive analysis. The findings of the research indicate the role of universities in the MHEWS is in line with the concept of tri dharma of universities which research, higher education and outreach. This means the role of universities to conduct research related to early warning systems, higher education through having program studies related to disaster management and outreach the role to train the community, organizations, and government through professional networks. Unfortunately, the involvement of the eastern part of Indonesian universities still needs to increase capacity, awareness, and access in utilizing the MHEWS at the local level.

Landslide early warning system prototype with GIS analysis indicates by soil movement and rainfall

The aim of this paper is developing and testing of landslide early warning system. The early warning system uses accelerometersas ground movement and tilt-sensing device and a water flow sensor. A microcentroller is used to process the input signal and activate the alarm. An LCD is used to display the acceleration in x,y and z axis. When the soil moved or shifted and rainfall reached 100 mm/day, the alarm rang and signal were sentto the monitoring center via a telemetry system.Data logging information and GIS spatial data can be monitored remotely as tables and graphics as well as in the form of geographical map with the help of web-GIS interface. The system were tested at Kampung Gerendong, Desa Putat Nutug, Kecamatan Ciseeng, Kabupaten Bogor. This area has 3.15 cumulative score, which mean vulnerable to landslide. The results show that the early warning system worked as planned.

Design and implementation of site-specific rainfall-induced landslide early warning and monitoring system: a case study at Nam Dan landslide (Vietnam)

ABSTRACTThis paper proposes and implements an early warning and monitoring system for rainfall-induced landslide (named as EWMRIL) with a case study at the Nam Dan landslide (northern Vietnam). The proposed system consists of six sensor nodes and one rainfall station that are used to sense large amounts of data in real-time such as soil moisture, pore-water pressure (PWP), movement status, and rainfall. In addition, a new flexible configuration for the wireless communication system is proposed that is capable not only to save the energy consuming but also to ensure the reliability of the system. Using wireless communication system, the sensed data were sent to the computer station for analyzing and predicting the instability of the landslide in terms of factor of safety (FoS) using the finite element seepage analysis and the limit equilibrium slope stability analysis methods. These methods are available in the SEEP/W and SLOPE/W modules of the GeoStudio software. Based on the analyzing results, the system proposed three warning levels for the landslide Early, Intermediate, and Imminent. Experiment result in the rainy season from August to September 2016 has proven the validity of the EWMRIL system. The result of this study is useful for landslide risk prevention and management in landslide prone-areas.

Optimized Vibration Chamber for Landslide Sensory and Alarm System

Landslide is one of natural hazard that is not unfamiliar disaster in Malaysia. Malaysia has experienced this disaster many times since 1969. This natural hazard has become a major research concern for Malaysian government when many people were injured badly and even had been killed. Many previous research works published in the open literature aimed at designing a system that could detect landslide in early stage before the landslide becomes catastrophic. This paper presents the early works on a major work-in-progress landslide early warning system for Malaysian environment. The aim of this system is to develop the most efficiently reliable cost-effective system in which slight earth movements are monitored continuously. The challenge this work aims at is to work with a low budget system that produces efficient performance. Hence, the material used is off-the-shelf. Early design optimization results of the vibration sensor used is quite promising detecting the slightest faint tremors, which are amplified using the best vibration chamber available. It is shown that the choice of proper pipe length and diameter dimensions in combination to a gravel to exaggerate the produced higher sensitivity level noise of 5 dB.

Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application

Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.