Landslide Early Warning Research Articles

Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China

Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a general strategy to effectively tackle the dangerous situation created by a giant dammed lake with 770 million cubic meters of water volume and formulate an emergency disposal solution for the 25 million cubic meters of debris, composed of engineering measures of floodgate excavation and non-engineering measures of reservoirs/hydropower stations operation. Such a disposal solution can not only reduce a large-scale flood (10,000-year return period, 0.01%) into a small-scale flood (10-year return period, 10%) but minimize the flood risk as well, guaranteeing no death raised by the giant landslide.

Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.

Successful early warning and emergency response of a disastrous rockslide in Guizhou province, China

Early warning of landslides is crucial for risk management and reduction, attracting a lot of attention from both scientists and stakeholders. However, it is challenging due to the complex nature of landslide behaviors and failure mechanisms. Here, we present a recent case of successful early warning and timely evacuation in advance of a large rockslide that occurred on 17 February 2019, in Guizhou Province, China. The rockslide was initially triggered in 2014 due to the excavation of slope toe for road expansion. Since then, the rockslide had become a potential threat to the local residents, pedestrians, and traffic. To ensure their safety, a wireless monitoring network combining on-site sensors and the geodetic method by Global Navigation Satellite System (GNSS) was installed to continuously monitor the surface displacement of the rockslide. Field monitoring data measured by crack gauges, rain gauges, and tiltmeter were transmitted to a real-time early warning system developed using the new artificial intelligence by the authors’ institute. Since the deformation of the rock mass was found increasing, nearby residents were evacuated immediately. Using predefined early warning thresholds for rockslides in the system, the rockslide was successfully forecasted 53 min in advance. Prompt action taken by scientists and local authorities averted human and economic losses completely. In this study, we introduce the real-time early warning system, its concept, the method for determining warning threshold, and performance, followed by the emergency mitigation measures performed for this particular rockslide. It is the 5th time our early warning system successfully forecasted a landslide since its implementation in 2017, and hence we discuss the key characteristics of the system in order to make it applicable for other cases globally.

Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China

Prediction on landslide displacement plays an important role in landslide early warning. Many models have been proposed for this purpose. However, the accuracy of the prediction results by these models often varies under different conditions. Rational evaluation and comprehensive consideration of these results still remain a scientific challenge. A new comprehensive combination model is proposed to predict the landslides displacement. The elementary displacement prediction is made by the support vector machine model, the exponential smoothing model, and the gray model (GM)(1,1). The results of the models are comprehensively evaluated by combining the results and introducing the accuracy matrix. The optimal weight in the evaluation work is obtained. A rational prediction result can be attained based on the so-called combination model. The proposed method has been tested by the application of Qinglong landslides in Guizhou Province, China. The comparison between the prediction results and in situ measurement shows that the prediction precision of the proposed model is satisfactory. The root-mean-square error (RMSE) of the combination model can be reduced to 1.4316 (monitoring site JCK2), 1.2623 (monitoring site JCK4), 2.3758 (monitoring site JCK6), 2.2704 (monitoring site JCK8), 1.4247 (monitoring site JCK11), and 0.9449 (monitoring site JCK12), which is much lower than the RMSE of the individual models.

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>

Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard Assessment

Soil moisture is important in the triggering of many types of landslides. However, in situ soil moisture data are rarely available in hazardous zones. The advanced remote sensing technology could provide useful soil moisture information. In this study, an assessment has been carried out between the latest version of the European Space Agency Climate Change Initiative soil moisture product and the landslide events in a northern Italian region in the 14-year period 2002–2015. A clear correlation has been found between the satellite soil moisture and the landslide events, as over four-fifths of events had soil wetness conditions above the 50% regional soil moisture line. Attempts have also been made to explore the soil moisture thresholds for landslide occurrences under different environmental conditions (land cover, soil type, and slope). The results showed slope distribution could provide a rather distinct separation of the soil moisture thresholds, with thresholds becoming smaller for steeper areas, indicating dryer soil condition could trigger landslides at hilly areas than in plain areas. The thresholds validation procedure is then carried out. Forty five rainfall events between 2014 and 2015 are used as test cases. Contingency tables, statistical indicators, and receiver operating characteristic analysis for thresholds under different exceedance probabilities (1%–50%) are explored. The results have shown that the thresholds using 30% exceedance probability provide the best performance with the hitting rate at 0.92 and the false alarm at 0.50. We expect this study can provide useful information for adopting the remotely sensed soil moisture in the landslide early warnings.

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.

Wireless self-powered sensor networks driven by triboelectric nanogenerator for in-situ real time survey of environmental monitoring

Reusing waste materials to create energy harvester will not just reduce the environmental pollution, but also generate renewable electricity by scavenging environmental energy to power wireless electronic devices, which will make our life more environmental friendly. In this study, we first turn waste milk carton into fully packaged arc-shaped triboelectric nanogenerator (AS-TENG) which can scavenge both wind and water flow energy for long-term wireless environmental monitoring. In general, the milk carton is laminated (from inside out) with a layer of polyethylene (PE), a layer of aluminum (Al) foil and a pulp layer. Such laminated material is suitable as the electrical unit of the AS-TENG. The arc-shaped design of AS-TENG is implemented to disrupt the flow, leading to vortex induced vibration, and thus contact electrification of the AS-TENG results in power generation. By optimizing the structural parameters systematically, the AS-TENG achieves an instantaneous peak voltage of 600 V, and a current of 40 μA. Moreover, a self-powered wireless environmental monitoring system using AS-TENG as power supply is developed for in-situ real time water quality (pH value) monitoring and landslide early warning in natural environment. This study provides solid progress toward the practical applications of TENGs in environmental monitoring.

Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning

Early warning for rainfall-induced shallow landsliding can help reduce fatalities and economic losses. Although these commonly occurring landslides are typically triggered by subsurface hydrological processes, most early warning criteria rely exclusively on empirical rainfall thresholds and other indirect proxies for subsurface wetness. We explore the utility of explicitly accounting for antecedent wetness by integrating real-time subsurface hydrologic measurements into landslide early warning criteria. Our efforts build on previous progress with rainfall thresholds, monitoring, and numerical modeling along the landslide-prone railway corridor between Everett and Seattle, Washington, USA. We propose a modification to a previously established recent versus antecedent (RA) cumulative rainfall thresholds by replacing the antecedent 15-day rainfall component with an average saturation observed over the same timeframe. We calculate this antecedent saturation with real-time telemetered measurements from five volumetric water content probes installed in the shallow subsurface within a steep vegetated hillslope. Our hybrid rainfall versus saturation (RS) threshold still relies on the same recent 3-day rainfall component as the existing RA thresholds, to facilitate ready integration with quantitative precipitation forecasts. During the 2015–2017 monitoring period, this RS hybrid approach has an increase of true positives and a decrease of false positives and false negatives relative to the previous RA rainfall-only thresholds. We also demonstrate that alternative hybrid threshold formats could be even more accurate, which suggests that further development and testing during future landslide seasons is needed. The positive results confirm that accounting for antecedent wetness conditions with direct subsurface hydrologic measurements can improve thresholds for alert systems and early warning of rainfall-induced shallow landsliding.

Feasibility of Using Elastic Wave Velocity Monitoring for Early Warning of Rainfall-Induced Slope Failure.

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate.

Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria

Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.

Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area, China

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area.

Landslide Emergency Monitoring and Early Warning Based on Situation Awareness Sensing

Compared with the traditional landslide monitoring and early warning technology, landslide emergency monitoring and early warning based on situation awareness sensing has the advantages of active sensing, real-time online diagnosis, combination of timing and triggering acquisition and monitoring. Through the comparison and analysis of the engineering application and traditional technology, this paper shows that the system applies the concept of situation awareness to the field of landslide emergency monitoring effectively and accurately. Besides, the vibration and tilt change of the landslide deformation body is obviously earlier than that of displacement, so it is better to monitor the vibration and tilt change of the landslide deformation body than the change of the displacement of the landslide deformation body.

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.

Real time monitoring of soil moisture content for landslide early warning: wn experimental study

Landslides occur commonly after heavy rainfall. More accurate and immediate prediction of landslides for early warning purpose can be achieved when real time water content of the soil slope is known. In this experimental study, the water content was measured using time domain reflectometers (TDR). Five TDRs were installed with equal vertical spacing in a test pit. The measured results were sent to and stored on a web server and real time monitoring was made online. All TDRs results showed a good and accurate water content response of the soil to the rainfall from top to the bottom of the test pit.

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.

Deriving the rainfall threshold for shallow landslide early warning during tropical cyclones: a case study in northern Philippines

Rainfall-induced landslides (RILs) have been a source of social and economic disruption in the mountainous Baguio area in northern Philippines. Prolonged heavy rainfall usually happens during tropical cyclone and southwest monsoon activity. A pragmatic approach to RIL mitigation is to develop rainfall-based early warning. We implemented a modified regression method to derive the empirical minimum intensity (I)–duration (D) threshold I = 6.46 D −0.28 and a normalized ID threshold NI = 0.002 D −0.28 for rainfall duration ranging between 24 and 264 h. Using a separate data set to evaluate the applicability of the threshold, 93% of the landslide-triggering rainfall events fell above the derived threshold. RILs also occurred when 24-h rainfall was 0.02–28% of the mean annual precipitation or after accumulating at least 500 mm of rainfall from the onset of the rainy season. The thresholds may be further refined as more landslide data become available in the future.

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.

An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning

To help reduce the impact of geo-hazards, an innovative landslide early-warning technology based on an energy demodulation-based fiber optic sensing (FOS-LW for short) technology, is introduced in this paper. FOS-LW measures the energy change in a sensing fiber at the segment of micro-bending, which can be caused by landslide movements, and automatically raises an alarm as soon as the measured signal intensity in the fiber reaches a pre-set threshold. Based on the sensing of micro-bending losses in the fiber optics, a two-event sensing algorithm has been developed for the landslide early-warning. The feasibility of the FOS-LW technology is verified through laboratory simulation and field tests. The result shows that FOS-LW has some unique features—such as the graded alarm, real-time responses, remote monitoring, low cost and passive optical network—and can be applied in the early-warning of landslides.