Landslide Early Warning System Research Articles

Integrating real-time sensor data for improved hydrogeotechnical modelling in landslide early warning in Western Himalaya

This study presents a comprehensive investigation into developing a real-time monitoring framework for a localized landslide early warning system (LEWS), focusing on the hydrological dynamics of an unsaturated slope near the Joshimath Badrinath Highway in Uttarakhand, India. Considering the steepness of the slope and its adjacency to the highway, continuous monitoring and stability assessment are crucial. The framework encompasses primary phases of monitoring, modelling, forecasting, and warning. Monitoring collected hydrological and meteorological data, used for modelling and calibration. Validation using Taylor diagrams ensured accuracy by comparing predicted and monitored data. The calibrated hydrological model guided slope stability modelling to identify instability factors. A machine learning algorithm detected potential instability. Forecasting predicted unstable periods, triggering warnings if the factor of safety (Fs) values drop below 1.5. The study provided important insights into slope stability, highlighting the significance of vegetation parameters in accurately assessing slope stability using the Fs. Calibration of the hydrogeological model, particularly considering rainfall, climate, and vegetation data, improved the alignment between predicted and observed volumetric water content (VWC), especially in shallower depths. However, modelling hydrological dynamics at greater depths remains challenging, emphasizing the need for refined approaches. Machine learning techniques, specifically a Random Forest model, achieve high accuracy in predicting Fs, identifying VWC at 0.3-m and 3-m depths as pivotal variables. Temporal evaluation suggests a 12-month simulation as optimal, showing consistent performance across depths. Overall, the study advances slope stability modelling and offers insights into sustainable slope management practices, highlighting the complex interplay between climate, vegetation, and hydrological dynamics in unsaturated slopes. This approach lays a foundation for effective LEWS in Indian Himalayan states.

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

Insights into the development of a landslide early warning system prototype in an informal settlement: the case of Bello Oriente in Medellín, Colombia

Abstract. The global number of vulnerable citizens in areas of landslide risk is expected to increase due to the twin forces of climate change and growing urbanization. Self-constructed or informal settlements are frequently built in hazardous terrain such as on landslide-prone slopes. They are characterized by high dynamics of growth, simple construction methods and strong social dynamics and are exposed to unsteady political approaches. Landslide early warning systems (LEWSs) can contribute to decreasing their vulnerability, but precise, affordable and culturally integrated LEWSs need to be further developed. In this paper, we present a 4-year living-lab research project called Inform@Risk that aimed to develop a LEWS prototype in the neighborhood of Bello Oriente, located on the urban–rural border of Medellín, Colombia. Its research team is composed of landscape architects, geo-engineers, and remote sensing and geo-informatics experts. The research team collaborated with a multitude of stakeholders: civil society, private enterprises, non-governmental agencies and various branches of government. A preliminary LEWS with the last functionalities still to be developed has been designed, implemented and handed over to the government. It has entered a test and calibration phase (i.e., warning-threshold development, procedures for warning and alert dissemination through the sensor system), which is on hold due to legal constraints. Our first findings indicate that the integrative development of technical aspects of a LEWS in informal settlements can be challenging, albeit manageable, whereas the level of social and political support is beyond the control of the designer. Steady political will is needed to increase technical capacities and funding of the operation and maintenance of an increased amount of monitoring equipment. Social outreach has to be continuous in order to inform, train, maintain the trust and increase the self-help capacities of the often rapidly changing population of an informal settlement. Legal requirements for a transfer of academic research projects to municipal authorities have to be clear from the start. Satisfying replacement housing options for the case of evacuation have to be in place in order to not lose the overall acceptance of the LEWS. As political will and municipal budgets can vary, a resilient LEWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality even in a reduced-governmental-support scenario.

Active waveguide deformation dynamics using acoustic emission technology for landslide early warning system

Active waveguide deformation dynamics using acoustic emission technology for landslide early warning system

Building sustainable and resilient communities with landslide early warning systems: A review of best practices

Introduction. Landslides are an important natural phenomenon that can have significant impacts on human communities and the environment. They can cause property damage, injury, and loss of life, making it crucial to understand their potential hazards. Materials and Methods. In this study, a systematic search was performed on Scopus database to collect relevant literature on community-based landslide early warning systems from global sources. The search utilized various keywords, including “Integrated early warning system,” “landslide early warning systems,” “community-based early warning systems,” “people-centered early warning systems,” and “resilient early warning systems.” Additionally, the snowballing technique was employed to identify further literature from the reference lists of the initial papers obtained through the initial search. Results. Totally, 157 articles, including journal publications, manuals, guidelines, books, and early warning systems, were reviewed. To enhance the discussion and comprehension of integrated early warning systems, literature without a community-based aspect was also examined. The literature was categorized into different sections such as the significance of landslide early warning systems, definitions for community-based early warning systems, and community-based landslide early warning systems for various regions worldwide, such as Africa, Asia, the Caribbean, Central America, Europe, North America, Oceania, and South America. The primary aim was to explore the utilization of community-based early warning systems by various stakeholders, including researchers, policymakers, and non-governmental organizations. Additionally, the literature on community-based landslide early warning systems was analyzed to identify methodologies used in designing the systems, which could be adapted to develop different technologies and methodologies in different regions of the world. Discussion. The given region-wise studies highlight the development and implementation of community-based landslide early warning systems (CBLEWS) in different parts of the world. These studies emphasize the importance of community involvement in the identification and management of landslide risks and the development and implementation of early warning systems. The papers present case studies that demonstrate the successful implementation of CBLEWS and its effectiveness in reducing the risk of landslides. Conclusion. The present paper emphasizes the importance of community engagement in the development and implementation of landslide early warning systems. Best practices in the establishment of such systems were reviewed, and community-based approaches were highlighted as crucial for disaster risk reduction efforts. It stresses the potential of early warning systems to prevent loss of life and property damage, promoting the creation of sustainable and resilient communities. Resume. While landslides pose a significant threat, particularly in resource-limited developing communities, Community-Based Landslide Early Warning Systems (CBLEWS) have emerged as a promising solution. This review highlights the effectiveness of CBLEWS in reducing fatalities, injuries, and infrastructure damage. Its success hinges on community participation and ownership. Suggestions for practical applications and future research directions. To empower communities facing landslides, successful CBLEWS models, hazard training, stakeholder collaboration, and context-specific warnings are crucial. Long-term impact studies, cost-benefit analyses, understanding social influences, integrating with other warning systems, exploring advanced technologies, and establishing global guidelines are key research areas for further strengthening CBLEWS and safeguarding communities.

Cost estimation for the monitoring instrumentation of landslide early warning systems

Abstract. Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed.

Stability analysis of rainfall-induced landslides: A case study of a hilly area in Bangladesh

Rainfall-induced landslides stand as the prevailing geohazard in tropical and subtropical regions. The ongoing global climatic changes have introduced a wave of anomalous rainfall events across the world. These abnormal weather patterns have triggered numerous landslide incidents, resulting in significant loss of life and property. To develop early warning systems for such threats to people in mountainous areas of Chittagong, Bangladesh, it requires an in-depth understanding of the geo-environmental properties of soil slopes under heavy rainfall. Historical records reveal that rainfall in total exceeding 150–250 mm has resulted in both localized slope collapses and debris floods. When it rains heavily, rainwater infiltrates initially unsaturated slopes, elevating the degree of saturation while decreasing soil suction. Consequently, the reduced shear strength along the critical slip surface diminishes the factor of safety (FoS), and slope failure occurs. As a result, studies of soil-water characteristics have been conducted, followed by laboratory testing at the impacted sites, and the study found that rainfall and soil type (fine-grained silt) were the most critical factors behind landslides. The mechanism of slope failure is also demonstrated using a simple numerical infiltration model and a slope stability equation. The soil characteristics, the saturated volumetric water content (θs), closely match the field water holding capacity (θf), with a minor difference of 4-8%, and the drainage condition at the bottom of the slope appear to be important factors. Application of these test results to an early warning system for landslides and flash floods is finally demonstrated based on some simplifying assumptions.

Climate Change Impacts on Shallow Landslide Events and on the Performance of the Regional Shallow Landslide Early Warning System of Piemonte (Northwestern Italy)

Shallow landslides are one of the most dangerous gravitational phenomena. They are responsible for more than 50% of causalities due to landslides in northwestern Italy in the last century. The aim of the research study presented here is focused on understanding if and how climate change influences the occurrence and behavior of this landslide type. A total of 120 widespread shallow landslide events have been analyzed from 1960 to 2019, taking into account the spatial and time distribution in association with related rainfall historical data elaborated by the Optimal Interpolation (OI) model. Results underline that shallow landslide events’ number (aggregated per five-year intervals) is characterized by a weak trend consisting of a slight increase in the Alps and a more pronounced decrease in the hilly and Apennines environments. In addition, the trend of the annual accumulated rainfall average shows a weak drop in the winter season of about 9 mm in ten years. Moreover, the rainy days have generally decreased over the hills and Apennines, while in the Alps, only in the summer season, with a decreasing rate of about 1.5 days every ten years. The rainfall trends are in accordance with those of shallow landslide events, pointing out the close and direct dependence of the shallow landslide events on the rainfall regime variations. The results obtained were also used to validate the robustness of the performance of the Regional Shallow Landslide Early Warning System adopted in Piemonte over the investigated period, confirming the effectiveness of the trigger thresholds used for the entire historical series and for different geographical areas.

Optimization of rainfall thresholds for landslide early warning through false alarm reduction and a multi-source validation

AbstractThis study proposes an innovative approach to develop a regional-scale landslide forecasting model based on rainfall thresholds optimized for operational early warning. In particular, it addresses two main issues that usually hinder the operational implementation of this kind of models: (i) the excessive number of false alarms, resulting in civil protection system activation without any real need, and (ii) the validation procedure, usually performed over periods too short to guarantee model reliability. To overcome these limitations, several techniques for reducing the number of false alarms were applied in this study, and a multiple validation phase was conducted using data from different sources. An intensity-duration threshold system for each of the five alert zones composing the Liguria region (Italy) was identified using a semiautomatic procedure called MaCumBA, considering three levels of criticality: low, moderate, and high. The thresholds were developed using a landslide inventory collected from online newspapers by a data mining technique called SECaGN. This method was chosen to account for only those events that echo on the Internet and therefore impact society, ignoring landslides occurred in remote areas, not of interest for civil protection intervention, which would adversely affect the model performance because they would result in false alarms. A calibration phase was performed to minimize the impact of false alarms, allowing at least one false alarm per year over the moderate criticality level. In addition, an innovative approach to include antecedent rainfall as the third dimension of the intensity-duration thresholds was applied, generating a consistent reduction in false alarms. The results were validated through an independent landslide inventory and were compared with (i) the alert issued by the regional civil protection agency to observe the improvements achieved with the proposed model and to evaluate to what extent the proposed model is consistent with the assessments of the civil protection and (ii) a dataset of the national states of emergency to verify the suitability of the developed thresholds for alerting citizens. The thresholds obtained showed high predictive capabilities, confirming their suitability for implementation in an operational landslide early warning system.

Application of a Web-Based Landslide Mitigation Information System (Sensor-Ku) in Kalongan Village Semarang, Central Java

Background and Objectives: Kalongan is one of the villages in Ungaran District with a potential landslide risk of 2% of its territory. Landslides occur along roadways and in residential areas, endangering the environment and damaging the infrastructure. A web-based early warning system application is the Landslide Early Warning System (Sensor-Ku) which is applied at the research location. This study intends to detect and provide real-time information on the likelihood of landslides, specifically to the people of Kalongan Village.
 Methods: This research creates a web-based monitoring and early warning system, as well as its implementation on an experimental microservice architecture, to provide landslide risk mitigation information to Kalongan residents.
 Results: According to the research, the landslide early warning system (LEWS) provides a solution by integrating many modules, such as recording dynamic variability, handling multi-scale data sets, extracting important trigger information about the onset of potential landslides, and broadcasting multi-level data. warning. This research also combines a real-time landslide early warning system. 
 Conclusion: The Landslide Early Warning System (LEWS) Program is effective in reducing the risk of landslides. Some of the issues found when running the program include the costs of maintaining the landslide early warning equipment, which must be managed by the Kalongan village community.

Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

ABSTRACT Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.

Regional-scale InSAR investigation and landslide early warning thresholds in Umbria, Italy

In this work, we use conventional two-pass differential satellite interferometry for the investigation of active landslides at regional scale in the Umbria Region of Italy. First, we use InSAR for the detection of active slope movements during the study period (October 2019 to January 2021). Then, we characterize the displacement rates and their variation through time for selected active slopes, by analysing 6-days interferograms. The evolutionary trends of the phenomena are compared to the pertaining climate and soil parameters that are used by the regional landslide early warning system (LEWS) to predict the possibility of landslide occurrence over the territory. We aim to explore the potential of differential interferometry to detect and monitor active landslides and, through that, to validate a geographical LEWS:Through the analysis of stacked interferograms, we identified 256 InSAR deformation signals (IDS) corresponding to active slope movements. Their distribution is compared to the geological map and the landslide inventory, illustrating how active landslides are favoured by weak lithologies and pre-existing slope instability. Also, the relative orientation of the satellite's line of sight with respect to the slope influences the results indicating a bias in the completeness of remotely sensed data. However, in the context of land management and civil protection, the results constitute a valuable information dataset for plans managed by local authorities.The analysis, at the interferogram scale (6-days), of 13 clearly recognizable IDSs illustrates the dynamics of the slopes through the variation of the displacement rates. Due to decorrelation, only about half of the interferograms can be used for this purpose, and interpretation is required. The comparison to the alert thresholds of the Umbria region shows the remarkable relationship between InSAR-derived landslide activity and the parameters used for landslide prediction (i.e., 48 h rainfall and soil moisture conditions). The maximum 48 h cumulative rainfall and the soil saturation index prove effective to predict landslide reactivations and accelerations. We provide uncertainty measures of the landslide prediction through binary classification metrics. Such results are only preliminary, given the limited number of landslides and timespan of our dataset. However, it represents one of the few attempts to validate the forecast of a regional LEWS quantitatively.

Exploring the Use of Pattern Classification Approaches for the Recognition of Landslide-Triggering Rainfalls

Rainfall-triggered landslides are well-known natural hazards that pose significant risks, and lot of effort has been invested to reduce the risk associated with this type of phenomenon. One approach to reduce such risk is the establishment of landslide early warning systems (LEWSs). LEWSs are designated to proactively identify conditions favorable to the initiation of landslides. When dealing with regional scale works, LEWSs are usually based on statistical methodologies to determine the minimum amount of rainfall required to trigger a landslide. This amount is often expressed in terms of minimum intensity or cumulative rainfall in a given time period. This research explores the use of artificial intelligence, specifically Long Short-Term Memory (LSTM) networks to analyze rainfall time series as either likely or not likely to result in a landslide. Various lengths of time series and different configurations of the model were tested to identify the best setting of the model. To develop the research, the selected test site was the Emilia-Romagna region in Italy, which has a robust landslide inventory, with assessed accuracy. Model performances were evaluated using several statistical indicators, including sensitivity (0.9), specificity (0.8), positive prediction power (0.82), negative prediction power (0.89), Efficiency (0.85) and misclassification rate (0.15). These results showed that the defined model correctly identified the rainfall conditions associated with landslide initiation with a high degree of accuracy and a low rate of false positives. In summary, this research demonstrates the potential of artificial intelligence, particularly LSTM networks, in improving the accuracy of LEWSs by analyzing rainfall time series data, ultimately enhancing our ability to predict and mitigate the risks of rainfall-triggered landslides.

Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil

Climate change has increased the frequency of extreme weather events and, consequently, the number of occurrences of natural disasters. In Brazil, among these disasters, floods, flash floods, and landslides account for the highest number of deaths, the latter being the most lethal. Bearing in mind the importance of monitoring areas susceptible to disasters, the REMADEN/REDEGEO project of the National Center for Monitoring and Natural Disaster Alerts (Cemaden) has promoted the installation of a network of soil moisture sensors in regions with a long history of landslides. This network was used in the present paper as a base to develop a system for moisture forecasting in those critical zones. The time series of rainfall and moisture were used in an inversion algorithm to obtain the geotechnical parameters of the soil. Then the geotechnical model was used in a forward calculation with the rainfall prediction to obtain the soil moisture forecast. The landslide events of March 2020 and May 2022 in Guarujá and Recife, respectively, were used as study cases for the developed system. The obtained results indicate that the proposed methodology has the potential to be used as an important tool in the decision-making process for issuing landslide alerts.

The Mechanical Characterization of Pyroclastic Deposits for Landslide Early Warning Systems

Broad mountainous areas in the western Campania (southern Italy), where young pyroclastic deposits extensively outcrop, frequently experience rainfall-induced slope movements of different degrees of mobility, causing heavy damage and fatalities. Such landslides cannot be easily mitigated, and the implementation of physically based early warning systems is still not able to predict the post-failure evolution of slope movements and the exposed areas at risk. This paper is devoted to overcoming this limit. To this end, the mechanical characterization of pyroclastic soil, carried out through an extensive laboratory testing program, is presented and compared with those of two other ashy soils of different depositional mechanisms. The results show that the depositional mode influences soil properties; to begin with, it affects the unsaturated shear strength, whose intercept of cohesion is up to 5 kPa higher in ashes of flow deposition than in airfall ash deposits. The saturated undrained soil response allowed for the identification of different levels of susceptibility to the liquefaction of pyroclastic deposits, which is one of the main factors governing the post-failure evolution of landslides. Gathering all the acquired information, including saturated and unsaturated soil shear strength, permeability function, and water retention curves, into a soil database, it was possible to present all data under a unitary framework. Finally, the implementation of the proposed flowchart for a simplified assessment of post-failure evolution to be employed in regional early warning systems can enhance our knowledge of the areas at risk.

Applying rainfall threshold estimates and frequency ratio model for landslide hazard assessment in the coastal mountain setting of South Asia

Landslides pose a serious risk to life and property in the mountainous regions around the globe. Understanding the interplay of landslide conditioning and triggering factors is essential for lessening the impacts caused by the hazard. Cox's Bazar — a coastal mountainous district in Bangladesh is recurrently affected by rainfall-triggered landslides. Based on analysis of 14 experiential landslides and combination of gauged and satellite rainfall estimates for the period from 2003 to 2019, the present study determines three landslide-triggering rainfall thresholds for the Cox's Bazar District (CBD): 1. Intensity-Duration (ID) threshold derived in this study revealed that any rainfall event with an intensity of ≥4.04 ​mm/h if prolonging for ≥12h can cause slope failures; 2. Event-Duration (ED) threshold suggested that a normalized cumulative event rainfall (EMAP) of 0.15 for one day is expected to trigger landslides; and 3. threshold calculated using randomly chosen antecedent rainfall expressed best distinction on 30-day rainfall and the equation of the threshold came out as Rth ​= ​64–0.02 Ra30. The recurrence probability of the derived antecedent rainfall threshold and likely landslides was determined through the Poisson distribution. Moreover, we assess the landslide susceptibility of the district with a coupled use of Frequency Ratio (FR) statistical measure and Geographic Information System (GIS). Considering the combined role of selected conditioning factors, the landslide susceptibility status of the CBD was quantified and classified into probability intervals. The accuracy of the susceptibility maps was assessed through the Relative Landslide Density Index (R-Index) that used a field landslide inventory, comprising well distributed 891 events. Moreover, gridded population data was superimposed on the derived susceptibility maps to understand the risk levels of people. The derivation of landslide-triggering rainfall thresholds and spatial susceptibility assessment has been useful to propose a low-cost Landslide Early Warning System (LEWS) which can contribute in alleviating the adverse effects of landslide hazard in the CBD.

Reducing uncertainties in hydromechanical modeling with a recently developed Rosetta 3 podeotransfer function

Stability analysis of unsaturated landslide deposits requires reliable estimates of soil moisture and pore water pressure. However, modeled soil moisture and pore water pressure contain substantial uncertainties due to imperfect information on soil hydraulic properties. Due to the relatively high dimensionality, commonly used parameter optimization strategies can be significantly affected by equifinality problems. This study investigates the effectiveness of reducing parameter estimation dimensionality using soil pedo-transfer functions. Specifically, we first estimated soil hydraulic parameters using the traditional Generalized Likelihood Uncertainty Estimation (GLUE) method, with parameters randomly drawn from the entire space (refer to as GLUE-random). In a second strategy, we use the Rosetta 3 pedotransfer function to constrain soil hydraulic parameters (refer to as GLUE-Rosetta). The two methods were tested in a typical landslide deposit with in-situ measured soil moisture dynamics for inverse modeling. The GLUE-random estimated soil hydraulic parameters contained substantial uncertainties –resulting in poorly constrained soil water retention curves (SWCC) and hydraulic conductivity functions (HCF). As a result, the uncertainty bands of pore water pressure and slope stability can cross values with several orders of magnitudes. In contrast, GLUE-Rosetta provided well-constrained SWCC and HCF, which significantly reduce the uncertainties in pore water pressure and slope stability estimates. These results suggest that the Rosetta 3 pedotransfer function can significantly improve the reliability of soil hydraulic parameters by reducing the dimensionality of the optimization problem and high-quality prior information of soil hydraulic properties. In conclusion, Rosetta 3 can enhance the reliability of soil parameters estimates and the reliability of subsurface hydrology, which may benefit the development of landslide early-warning systems.

Recommendations for Landslide Early Warning Systems in Informal Settlements Based on a Case Study in Medellín, Colombia

Recommendations for Landslide Early Warning Systems in Informal Settlements Based on a Case Study in Medellín, Colombia

Incorporating Rainfall Forecast Data in X-SLIP Platform to Predict the Triggering of Rainfall-Induced Shallow Landslides in Real Time

Extreme and prolonged rainfall resulting from global warming determines a growing need for reliable Landslide Early Warning Systems (LEWS) to manage the risk of rainfall-induced shallow landslides (also called soil slips). Regional LEWS are typically based on data-driven methods because of their greater computational effectiveness, which is greater than the ones of physically based models (PBMs); however, the latter reproduces the physical mechanism of the modelled phenomena, and their modelling is more accurate. The purpose of this research is to investigate the prediction quality of the simplified PBM SLIP (implemented in the X-SLIP platform) when applied on a regional scale by analysing the stability of rain forecasts. X-SLIP was updated to handle the GRIB files (format for weather forecast). Four real-time predictions were simulated on some towns of the Emilia Apennines (northern Italy) involved in widespread soil slips on 5 April 2013; specifically, maps of factors of safety related to this event were derived assuming that X-SLIP had run 72 h, 48 h, 24 h and 12 h in advance. The results indicated that the predictions with forecasts (depending on the forecast quality) are as accurate as the ones derived with rainfall recordings only (benchmark). Moreover, the proposed method provides a reduced number of false alarms when no landslide was reported to occur in the whole area. X-SLIP with rain forecasts can, therefore, represent an important tool to predict the occurrence of future soil slips at a regional scale.

Landslide displacement forecasting using deep learning and monitoring data across selected sites

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models’ remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv-LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS).