Topic Editors

Department of Earth and Marine Sciences, University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Department of Earth Sciences, University of Florence, Via La Pira, 4, 50121 Florence, Italy
Department of Earth Sciences, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy
Department of Earth and Marine Sciences, University of Palermo, Via Archirafi 22, 90123 Palermo, Italy

Landslide Prediction, Monitoring and Early Warning

Abstract submission deadline
closed (20 July 2023)
Manuscript submission deadline
closed (20 December 2023)
Viewed by
14060

Topic Information

Dear Colleagues,

Landslides are among the most severe nautral phenomena in terms of causing human and economic losses worldwide, so it is becoming increasingly urgent tooptimise mitigation strategies to reduce the costs related their occurrence. A number of reliable approaches are currently available for spatially assessing landslide susceptibility and/or hazard on the basin scale. At the same time, potential rainfall or seismic acceleration thresholds can be effectively investigated either by means of deterministic models or statistical approaches, exploiting the large diffusion of remote sensing technologies which have recently opened to scientists access to cyclical recurrent temporal images of the Earth’s surface. Crossing landslide prediction images with triggering threshold settings makes it possible to implement early warning systems suitable for tuning civil protection responses on the basis of the surveillance of seismic or rainfall-inducing factors.

This Topic aims at contributing to the bridging needed between landslide susceptibility/hazard assessment, slope failure models and related earthquake/rainfall triggering thresholds, ground deformation monitoring, and early warning systems. Multidisciplinary contributions are expected from statistical, hydrological, geotechnical, seismological, and geomatical approaches in aadition to contributions on issues related to applied geomorphology.

Prof. Dr. Edoardo Rotigliano
Dr. Pierluigi Confuorto
Dr. Michele Delchiaro
Dr. Chiara Martinello
Topic Editors

Keywords

  • landslide susceptibility/hazard assessment and mapping
  • rainfall/seismic acceleration threshold investigation
  • field/remote technologies for ground deformation monitoring
  • landslide early warning system
  • landslide risk analysis

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400
GeoHazards
geohazards
- - 2020 20.7 Days CHF 1000
Geosciences
geosciences
2.7 5.2 2011 23.6 Days CHF 1800
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700
Water
water
3.4 5.5 2009 16.5 Days CHF 2600

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Published Papers (10 papers)

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20 pages, 6209 KiB  
Article
A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network
Water 2023, 15(24), 4247; https://doi.org/10.3390/w15244247 - 11 Dec 2023
Cited by 2 | Viewed by 736
Abstract
Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural network (TCN) and a bidirectional long short-term memory neural network (BiLSTM) to [...] Read more.
Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural network (TCN) and a bidirectional long short-term memory neural network (BiLSTM) to address the shortcomings of traditional recurrent neural networks in predicting displacement-fluctuation-type landslides. Based on the idea of time series decomposition, the improved complete ensemble empirical mode decomposition with an adaptive noise method (ICEEMDAN) was used to decompose displacement time series data into trend and fluctuation terms. Trend displacement is mainly influenced by the internal geological conditions of a landslide, and polynomial fitting is used to determine the future trend displacement; The displacement of the fluctuation term is mainly influenced by the external environment of landslides. This article selects three types of landslide-influencing factors: rainfall, groundwater level elevation, and the historical displacement of landslides. It uses a combination of gray correlation (GRG) and mutual information (MIC) correlation modules for feature screening. Then, TCN is used to extract landslide characteristic factors, and BiLSTM captures the relationship between features and displacement to achieve the prediction of wave term displacement. Finally, the trend term and fluctuation term displacement prediction values are reconstructed to obtain the total displacement prediction value. The results indicate that the ICEEMDAN–TCN–BiLSTM model proposed in this article can accurately predict landslide displacement and has high engineering application value, which is helpful for planning and constructing landslide disaster prevention projects. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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28 pages, 18849 KiB  
Article
Adaptability Analysis of Sentinel−1A and ALOS/PALSAR−2 in Landslide Detection in the Qinling-Daba Mountains
Appl. Sci. 2023, 13(21), 12080; https://doi.org/10.3390/app132112080 - 06 Nov 2023
Viewed by 639
Abstract
Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a [...] Read more.
Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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35 pages, 87769 KiB  
Article
Mass-Movement Causes and Landslide Susceptibility in River Valleys of Lowland Areas: A Case Study in the Central Radunia Valley, Northern Poland
Geosciences 2023, 13(9), 277; https://doi.org/10.3390/geosciences13090277 - 13 Sep 2023
Viewed by 1042
Abstract
This work aims to analyse the mechanisms and factors contributing to shallow soil landslides in river valleys entrenched in lowlands on the example of the Central Radunia Valley. The combination of susceptibility analysis using geographic-information-system-based statistical models, field surveys, analysis of archival materials, [...] Read more.
This work aims to analyse the mechanisms and factors contributing to shallow soil landslides in river valleys entrenched in lowlands on the example of the Central Radunia Valley. The combination of susceptibility analysis using geographic-information-system-based statistical models, field surveys, analysis of archival materials, and numerical modelling for the analysis of slope stability and hydrogeological processes allows for comprehensive landslide reconstruction, mass movement mechanism description, and an explanation of the role of triggering and causal factors. The results emphasise the need for cross-disciplinary studies of shallow soil landslides. The identification and prioritisation of the causal factors indicate that geomorphological conditions play a particularly important role. The current study shows that the greatest influence on landslide formation in the Central Radunia Valley is slope angle, as determined using a high-resolution digital elevation model. The slope angle factor is sufficient to produce a reliable susceptibility map (the areas under the curve of the success rate and prediction rate curves are 87.84% and 85.34%, respectively). However, numerical modelling of slope failure also clearly indicated that there was a significant influence of anthropogenic impacts on the landslide process. We determined that the main triggering factor causing the January 2019 Rutki landslide was related to the drilling of a borehole on 10 January 2019. The water used for drilling hydrated the soil and thus weakened the stability conditions. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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23 pages, 10543 KiB  
Article
Impact of Vegetation Differences on Shallow Landslides: A Case Study in Aso, Japan
Water 2023, 15(18), 3193; https://doi.org/10.3390/w15183193 - 07 Sep 2023
Cited by 2 | Viewed by 1216
Abstract
Climate change has increased the frequency and scale of heavy rainfall, increasing the risk of shallow landslides due to heavy rainfall. In recent years, ecosystem-based disaster risk reduction (Eco-DRR) has attracted attention as one way to reduce disaster risks. Vegetation is known to [...] Read more.
Climate change has increased the frequency and scale of heavy rainfall, increasing the risk of shallow landslides due to heavy rainfall. In recent years, ecosystem-based disaster risk reduction (Eco-DRR) has attracted attention as one way to reduce disaster risks. Vegetation is known to increase soil strength through its root system and reduce the risk of shallow landslides. To reduce the risk of shallow landslides using vegetation, it is necessary to quantitatively evaluate the effects that vegetation has on shallow landslides. In this study, we constructed a generalized linear model (GLM) and random forest (RF) model to quantitatively evaluate the impact of differences in the vegetation, such as grasslands and forests, on the occurrence of shallow landslides using statistical methods. The model that resulted in the lowest AIC in the GLM included elevation, slope angle, slope aspect, undulation, TWI, geology, and vegetation as primary factors, and the hourly rainfall as a trigger factor. The slope angle, undulation, and hourly rainfall were selected as significant explanatory variables that contribute positively to shallow landslides. On the other hand, elevation and TWI were selected as significant explanatory variables that contribute negatively to shallow landslides. Significant differences were observed among multiple categories of vegetation. The probability of shallow landslide in secondary grasslands was approximately three times that of coniferous and broadleaf forests, and approximately nine times that of broadleaf secondary forests. The landslide probability of shrubs was approximately four times that of coniferous and broadleaf forests, and approximately ten times that of broadleaf secondary forests. The results of constructing the RF model showed that the importance was highest for the hourly rainfall, followed by geology, then elevation. AUC values for the GLM and RF model were 0.91 and 0.95, respectively, indicating that highly accurate models were constructed. We quantitatively showed the impact of differences in vegetation on shallow landslides. The knowledge obtained in this study will be essential for considering appropriate vegetation management to reduce the risk of future shallow landslides. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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20 pages, 13054 KiB  
Article
Predisposing Factors for Shallow Landslides in Alpine and Hilly/Apennines Environments: A Case Study from Piemonte, Italy
Geosciences 2023, 13(8), 252; https://doi.org/10.3390/geosciences13080252 - 19 Aug 2023
Viewed by 1019
Abstract
Landslides are the most common natural hazard in the Piemonte region (northwestern Italy). This study is focused on shallow landslides caused by the sliding of the surficial detrital-colluvial cover caused by rainfall and characterized by a sudden and fast evolution. This study investigates [...] Read more.
Landslides are the most common natural hazard in the Piemonte region (northwestern Italy). This study is focused on shallow landslides caused by the sliding of the surficial detrital-colluvial cover caused by rainfall and characterized by a sudden and fast evolution. This study investigates shallow landslide events compared with variables considered as main predisposing qualitative factors (lithology, pedology and land use) to obtain a zonation of shallow landslide susceptibility in a GIS environment. Additionally, wildfire occurrence is also evaluated as a further predisposing factor for shallow landslide initiation. The resulting susceptibility map shows a strong correlation between the first three variables and shallow landslide occurrence, while it shows a negligible, or very localized, relationship with wildfire occurrence. Through the intersection of the predisposing factors with the landslide data points, a map of homogeneous zones is obtained; each identified zone is characterized by uniform lithological, soil-type, and land-use characteristics. The shallow landslide density occurrence is computed for each zone, resulting in a four-range susceptibility map. The resulting susceptibility zones can be used to define and evaluate the hazard linked to shallow landslide events for civil protection and regional planning purposes. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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27 pages, 21638 KiB  
Article
Sensitivity Evaluation of Time Series InSAR Monitoring Results for Landslide Detection
Remote Sens. 2023, 15(15), 3906; https://doi.org/10.3390/rs15153906 - 07 Aug 2023
Viewed by 1178
Abstract
Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those [...] Read more.
Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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26 pages, 10064 KiB  
Article
Hydro–Mechanical Behaviour of a Rainfall-Induced Landslide by Instrumental Monitoring: Landslide–Rainfall Threshold of the Western Black Sea Bartin Region of Türkiye
Appl. Sci. 2023, 13(15), 8703; https://doi.org/10.3390/app13158703 - 27 Jul 2023
Viewed by 757
Abstract
Bartin City is located in the Western Black Sea Region of Türkiye, where rainfall-induced landslides are more frequently observed. Although it is known that many landslides are induced by rainfall, there is limited knowledge regarding how rainfall triggers these landslides in the city. [...] Read more.
Bartin City is located in the Western Black Sea Region of Türkiye, where rainfall-induced landslides are more frequently observed. Although it is known that many landslides are induced by rainfall, there is limited knowledge regarding how rainfall triggers these landslides in the city. To clarify the triggering mechanisms of rainfall-induced landslides, a detailed field monitoring program was performed on a chosen area to represent landslides in Bartin. The instrumentation included the measurements of site suction, volumetric water content, groundwater level, and rainfall amount over a period of two years. Various stability analyses were performed regarding pore pressures after both transient flow infiltration analysis and site-measured suction values. The rainfall intensity–duration thresholds were obtained for both dry and wet periods as a result of the numerical analyses performed by means of parameters obtained from field monitoring. The results show that the wet period conditions create more critical conditions before failure compared to the dry period conditions, so landslides occur more easily in wet periods. According to the landslide–rainfall threshold relations, landslide-risk limits are reached if the rainfall intensity is over 10 mm/h for the dry periods and lasts between 0.85 h and 17 h depending on the saturated hydraulic conductivity of the soil. When the rainfall intensities are less than 10 mm/h, longer rainfall durations are needed for a landslide to occur. For the wet periods, landslide-risk situations are encountered if the rainfall intensity over 1 mm/h continues for 0.36 h–3.67 h, depending on the saturated hydraulic conductivities. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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17 pages, 3767 KiB  
Article
Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case
Water 2023, 15(14), 2514; https://doi.org/10.3390/w15142514 - 09 Jul 2023
Cited by 1 | Viewed by 1505
Abstract
The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these [...] Read more.
The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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12 pages, 5897 KiB  
Article
Investigating the Effects of Cell Size in Statistical Landslide Susceptibility Modelling for Different Landslide Typologies: A Test in Central–Northern Sicily
Appl. Sci. 2023, 13(2), 1145; https://doi.org/10.3390/app13021145 - 14 Jan 2023
Cited by 5 | Viewed by 1262
Abstract
Optimally sizing grid cells is a relevant research issue in landslide susceptibility evaluation. In fact, the size of the adopted mapping units influences several aspects spanning from statistical (the number of positive/negative cases and prevalence and resolution/precision trade-off) and purely geomorphological (the representativeness [...] Read more.
Optimally sizing grid cells is a relevant research issue in landslide susceptibility evaluation. In fact, the size of the adopted mapping units influences several aspects spanning from statistical (the number of positive/negative cases and prevalence and resolution/precision trade-off) and purely geomorphological (the representativeness of the mapping units and the diagnostic areas) to cartographic (the suitability of the obtained prediction images for the final users) topics. In this paper, the results of landslide susceptibility modelling in a 343 km2 catchment for three different types of landslides (rotational/translational slides, slope flows and local flows) using different pixel-size mapping units (5, 8, 10, 16 and 32 m) are compared and discussed. The obtained results show that the higher-resolution model (5 m) did not produce the best performance for any of the landslide typologies. The model with 8 m sized pixels displayed the optimal threshold size for slides and slope flows. In contrast, for local flows, an increasing trend of model prediction accuracy was reached with 32 m pixels, which was a higher value than that presented using 8 m pixels. The variable importance analysis demonstrated that the better performance of the 8 m cells was due to their effectiveness in capturing morphological conditions which favour slope instability (profile curvature and middle and high ridges). Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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21 pages, 18963 KiB  
Article
Landslide Displacement Prediction Based on Variational Mode Decomposition and GA–Elman Model
Appl. Sci. 2023, 13(1), 450; https://doi.org/10.3390/app13010450 - 29 Dec 2022
Cited by 1 | Viewed by 1516
Abstract
Landslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based on variational mode decomposition and a genetic algorithm optimization of [...] Read more.
Landslide displacement prediction is an important part of monitoring and early warning systems. Effective displacement prediction is instrumental in reducing the risk of landslide disasters. This paper proposes a displacement prediction model based on variational mode decomposition and a genetic algorithm optimization of the Elman neural network (VMD–GA–Elman). First, using VMD, the landslide displacement sequence is decomposed into the three subsequences of the trend term, the periodic term, and the random term. Then, appropriate influencing factors are selected for each of the three subsequences to construct input datasets; the rationality of the selection of the influencing factors is evaluated using the gray correlation analysis method. The GA–Elman model is used to forecast the trend item, periodic item and random item. Finally, the total displacement is obtained by superimposing the three subsequences to verify the performance of the model. A case study of the Shuizhuyuan landslide (China) is presented for the validation of the developed model. The results show that the model in this paper is in good agreement with the actual situation and has good prediction accuracy; it can, therefore, provide a basis for early warning systems for landslide displacement and deformation. Full article
(This article belongs to the Topic Landslide Prediction, Monitoring and Early Warning)
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