Impact Of Landslides Research Articles

Automatic detection of landslide impact areas using Google Earth Engine

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.

Hypermobility of a Catastrophic Earthquake-Induced Loess Landslide

Landslide mobility refers to how far and fast a landslide can move downslope. It controls landslide impact areas and damage power. Highly mobile landslides are often initiated on slopes steeper than 30°. However, on 18 December 2023, an earthquake-induced landslide (35°52′54″N, 102°51′10″E) exhibited extraordinary mobility, with an overall travel angle of 1.5°, breaking an on-land landslide record. The landslide originated on a gentle slope (3.6°), eroded an earth dam along its travel path, and finally destroyed 51 houses and claimed 20 lives. Remote sensing and field surveys were conducted to provide morphological characteristics of the hazard chain. A numerical program, EDDA (Erosion–Deposition Debris Flow Analysis), was employed to reproduce the flow dynamics and investigate the causes of hypermobility. The findings reveal three primary causes of hypermobility: (1) liquefaction of the saturated silty loess stratum due to the combined effects of irrigation activity and seismic loading, (2) the loose and macro-pore structure of loess, and (3) confined topography and icy channel bed. The mechanisms revealed have broad implications for understanding fluidized mass movements on gentle slopes in seismically active regions.

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

Advanced Landslide Detection Using Machine Learning and Remote Sensing Data

Landslides can cause severe damage to infrastructure and human life, making early detection and warning systems critical for mitigating their impact. In this study, we propose a machine learning approach for landslide detection using remote sensing data and topographical features. We evaluate the performance of several machine learning algorithms, including Tree, Random Forest, Gradient Boosting, Logistic Regression, Naïve Bayes, AdaBoost, Neural Network, SGD, kNN, and SVM, on a dataset of remote sensing images and topographical features from the Sikkim region in Malaysia. The results show that the SVM algorithm outperforms the other algorithms with an accuracy of 96.7% and a F1 score of 0.97. The study demonstrates the potential of machine learning algorithms for landslide detection, which can help improve early warning systems and reduce the impact of landslides.

Spatial Prediction of Landslide Susceptibility using Various Machine Learning Based Binary Classification Methods

ABSTRACT This study compares the performance of various machine learning models for predicting landslide susceptibility using a geospatial dataset from the Lai Chau province, Vietnam. The dataset consisted of 850 landslide locations and ten influencing factors. Eight models, including Forest by Penalizing Attributes (FPA), Bagging-based FPA (BFPA), Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Bayesian Network (BN), and Naïve Bayes (NB), were evaluated based on different evaluation metrics. The results revealed distinct variations in the performance of the models across the evaluation metrics. Based on the overall rankings, the ensemble BFPA model with sensitivity=90%, specificity= 95.98%, accuracy=92.86%, Kappa=0.857, and area under the curve=0.98 demonstrated the highest capability in predicting landslide susceptibility. It was followed by BN, FPA, MLP, ANN, SVM, LR, and NB. These findings suggest that the BFPA model outperformed other models in terms of its ability to accurately identify potential landslide-prone areas in the study region. This study provides valuable insights into the comparative analysis of machine learning models for landslide susceptibility prediction. Furthermore, it supports the effectiveness of ensemble models for landslide susceptibility prediction, which can inform decision-makers, land-use planners, and disaster management agencies in making informed decisions regarding potential landslide hazards and implementing effective risk mitigation strategies in Vietnam. Continued research in this area will enhance our understanding of machine learning techniques and their application in mitigating the impact of landslides on society and the environment.

A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility

The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples from landslide-free regions or outside the landslide buffer zones randomly and quickly but often ignore the reliability of non-landslide samples, which will pose a serious risk of including potential landslides and lead to erroneous outcomes in training data. Furthermore, diverse machine-learning models exhibit distinct classification capabilities, and applying a single model can readily result in over-fitting of the dataset and introduce potential uncertainties in predictions. To address these problems, taking Chenxi County, a hilly and mountainous area in southern China, as an example, this research proposes a strategy-coupling optimised sampling with heterogeneous ensemble machine learning to enhance the accuracy of landslide susceptibility prediction. Initially, 21 landslide impact factors were derived from five aspects: geology, hydrology, topography, meteorology, human activities, and geographical environment. Then, these factors were screened through a correlation analysis and collinearity diagnosis. Afterwards, an optimised sampling (OS) method was utilised to select negative samples by fusing the reliability of non-landslide samples and certainty factor values on the basis of the environmental similarity and statistical model. Subsequently, the adopted non-landslide samples and historical landslides were combined to create machine-learning datasets. Finally, baseline models (support vector machine, random forest, and back propagation neural network) and the stacking ensemble model were employed to predict susceptibility. The findings indicated that the OS method, considering the reliability of non-landslide samples, achieved higher-quality negative samples than currently widely used sampling methods. The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. The integrated strategy significantly improves the prediction of landslide susceptibility and makes it reliable and effective for assessing regional geohazard risk.

The influence of rainfall patterns on factor of safety for clayey soil slopes

The persistent trend of rising temperatures and shifting weather patterns caused by climate change has prompted significant concern around the world. This research aims to evaluate the instability of slopes in Almaty, Kazakhstan, under various rainfall patterns, groundwater tables, and slope geometries by incorporating the principles of unsaturated soil mechanics. However, there have been a limited number of studies incorporating the principle of unsaturated soil mechanics with constant rainfall patterns in Central Asia, particularly in Kazakhstan, on the impact of rainfall-causing landslides. Hence, in this research, GeoStudio software (SEEP/W and SLOPE/W) was used to simulate the factor of safety (FoS) and pore water pressure for the investigated slopes under different rainfall patterns. Results from Hyprop and statistical method show that the saturated volumetric water content is 0.502, whereas the residual one is 0.147 and for the permeability function the conductivity coefficient started to sharply decrease at the suction value of 2 kPa when the air-entry value was 24 kPa. Findings from numerical analysis show the change in FoS for the slope of 10 m height and 27-degree slope angle was 6%, 7%, 7%, and 8% for cyclic, delayed, advanced, and normal distributions, respectively. For the slope with 20 m height and the same 27-degree angle, the change in FoS was 8%, 10%, 8%, and 11% for the cyclic, delayed, advanced, and normal distributions, respectively. These same patterns were shown in slopes with 35-degree and 45-degree angles, having the same 10 m and 20 m heights. Comparatively, this shows that slopes under cyclic rainfall patterns (240 mm of rain within 12 days) are less prone to failure compared to slopes under continuous, delayed, or regularly distributed rainfall patterns. Moreover, an increase in slope height and angle also affect the FoS negatively. It should be noted that the results obtained are only applicable to clayey-loam soil.

Impact of floods and landslides on rhizosphere bacterial communities: a high-throughput 16S rRNA gene sequencing study.

This study investigates the diversity and composition of soil bacterial communities in the rhizosphere of Attapadi and Nelliyampathy, prominent hill stations in Palakkad district, Kerala, India. The persistent flooding and landslides in 2018 and 2019 significantly impacted agricultural productivity in these regions. Utilizing high-throughput 16S rRNA gene sequencing (Illumina MiSeq), we conducted a comprehensive analysis of soil samples. Correlative assessments between soil parameters and microbial relative abundance at the phylum level revealed noteworthy positive associations. Notably, nitrogen (N) exhibited a positive relation with Crenarchaeota, Chloroflexi, Actinobacteriota, and Acidobacteriota; pH correlated with Firmicutes; organic carbon (OC) with WPS-2; and phosphorous with Proteobacteria. A total of 31,402 operational taxonomic units (OTUs) were identified, with the highest feature counts observed in undisturbed soils from Attapadi (AUD) and Nelliyampathy (NUD) (13,007 and 12,915, respectively). Disturbed soils in Nelliyampathy (ND) and Attapadi (AD) displayed a substantial decline in microbial diversity and composition, harbouring 1409 and 4071 OTUs, respectively. Alpha and beta diversity indices further underscored the more severe impairment of ND soils compared to AD soils. Interestingly, a majority of ND samples were landslide-affected (four out of five), while flood-affected soils accounted for four out of six AD samples. This indicates that landslides exert a more pronounced impact on microbial diversity and composition than floods. The observed decline in microbial count, composition, and diversity, even after 2years of the disaster, raises concerns about potential threats to agricultural output. The findings emphasize the need for corrective measures, including the incorporation of microbial inoculum, to restore soil fertility in post-disaster landscapes.

Landslide susceptibility assessment using novel hybridized methods based on the support vector regression

Landslide susceptibility assessment is a complex task due to the multitude of causative factors, spatial variability, data availability, modeling uncertainty, and validation issues. This study addresses these challenges by proposing two predictive models that hybridize support vector regression (SVR) with two evolutionary algorithms: grey wolf optimizer (GWO) and cuckoo search algorithm (CSA). These models were developed using an extensive geospatial database from northern Iran. Over the training phase, the basic predictive model, developed using SVR, was enhanced by incorporating the GWO and CSA algorithms, resulting in the development of two hybridized models: SVR-GWO and SVR-CSA. Over the validation phase, the performance and effectiveness of each hybridized model were compared to the standalone SVR using several metrics. Compared to the standalone SVR model, the hybridized models demonstrated significant improvement in generalization and predictive abilities. Despite non-significant difference between the performances of the SVR-GWO and SVR-CSA models, the SVR-GWO model demonstrated superior performance. This could be attributed to the GWO's capabilities, which included generating a variety of solutions, demonstrating robustness against noise and outliers, achieving faster convergence speed, and effectively interacting with SVR. This study highlighted that utilizing intelligence hybridized models can significantly enhance the balance between accuracy, robustness, and objectives compared to single models. This finding holds significant implications for ecological engineers tasked with designing and implementing solutions to mitigate the impact of shallow landslides on the environment and human communities. The predictive models developed in this study serve as valuable tools for these engineers, enabling them to identify high-risk areas and implement preventative measures. This interdisciplinary approach, which combines machine learning, optimization algorithms, and ecological engineering, highlights the potential for pioneering solutions in tackling complex environmental challenges, thereby standing as a testament to the power of innovation in driving progress in landslide susceptibility assessment.

Soil Vulnerability Analysis Using the Microtremor Method for Mapping the Landslide Susceptibility Area in Batu City, Indonesia

Based on Regional Agency for Disaster Management (BPBD) Batu City data, Indonesia, 36 landslides have been recorded in the last three years around the Payung area, where this area connects several regions around Batu City. So, it is necessary to map areas with potential ground movement as an initial step for the local government in formulating a landslide disaster management strategy. This research aims to map landslide susceptibility areas around Payung, Batu City, identified based on the soil vulnerability index value range in the peak ground acceleration (PGA) mapping results. The data was measured using the microtremor method. There were 22 measurement points in 3 zones with different heights, and the microtremor signal was recorded for 35-40 minutes at each point. The microtremor signal from seismic recording is composed of three components (NS, EW, and Up-Down), which are used to develop the Horizontal to Vertical Spectral Ratio (HVSR) technique, where the ratio value is obtained by comparing horizontal (H) components (HNS and HEW) to the vertical component (Up-Down (V)). The research results show that landslide susceptibility areas are located at points M10, M13, and M16, with a soil vulnerability index range of 21-40.1. When a landslide occurs in that area with high vulnerability, it will impact points along Payung Street at points M3–M9. The points with high soil vulnerability index values need special treatment to reduce the impact of landslides.

Landslide hazard zonation (LHZ) mapping of Doon Valley using multi-criteria analysis method based on remote sensing and GIS techniques

The occurrence of landslides is a costly and cataclysmic natural hazard that mainly occurs in hilly areas due to factors like earthquakes, cloud burst, extreme rainfall, human pressure, etc. leading to loss of biodiversity, property, and life. Effective and comprehensive landslide risk management is crucial to address landslide susceptibility. Utilizing remote sensing and Geographic Information System (GIS) techniques, this work focuses on the landslide hazard zonation (LHZ) mapping. The study is specifically conducted in the Doon Valley. These advanced technologies help in identifying and categorizing the areas prone to landslides. The Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data at 30 m resolution and Sentinel-2B data at 10 m resolution were used to perform the remote sensing and GIS operations in ArcGIS Software. Thematic layers such as Land Use/Land Cover (LULC), Slope, Geology, Soil Type, Elevation, Drainage Density, Vegetation, and Aspect were produced utilizing remote sensing and GIS data. The weighted overlay, a multi-criteria analysis method, was applied to assign attribute values to each thematic layer based on their importance, which was then combined to calculate the landslide hazard zone. The findings of this study demonstrate that landslides are more likely to occur near and above the Main Boundary Thrust/Fault (MBT). The high to very high hazard zone covers 16.64% of the total area, making landslides occurring more frequently. Additionally, the study found that the Doon Valley rivers’ upper segments are more susceptible to landslides than their lower segments. By integrating Remote Sensing and GIS techniques, it is possible to obtain extensive knowledge of regions prone to landslides. This information will be helpful for decision-makers and planners to reduce the impact of landslides in the near future.

Analysis of sensitive clay landslide mechanism and impact pipeline bearing characteristics considering spatial variability

Landslides caused by the progressive failure of sensitive clay slopes may cause substantial economic losses and environmental pollution to pipelines. This study utilizes the Karhunen-Loève (K-L) expansion method, the coupled Eulerian-Lagrangian (CEL) method, and the Monte Carlo method to model the large deformation behaviour of slopes with spatial variability. The proposed RCEL-MC framework for joint analysis of extensive deformation-probability statistics of landslide impact on pipelines confirms that the spatial variability clay slope instability failure mechanism, considering the soil softening effect under earthquake action, aligns more closely with real-world scenarios. Furthermore, based on the statistical failure probability of pipelines impacted by landslides, a sensitive clay slope pipeline safety design method is suggested. The research highlights that the random field parameters significantly influence the load-bearing characteristics of pipelines affected by spatially variable slope progressive failure landslides. The uncertainty surrounding the maximum impact force on pipelines notably increases with the rise of the horizontal correlation length and variability coefficient. When accounting for spatial variability, the maximum impact force on landslide pipelines surpasses the deterministic model analysis results by 28-69 %. It is emphasized that the deterministic model analysis, which neglects the spatial variability of soil and the soil softening effect, may seriously underestimate landslide risk. Based on the criteria for pipeline buckling failure and yield failure, it is advised that the safe design parameters for pipelines in practical projects should fall within the intersection of the secure regions defined by the two failure criteria.

Investigating landslide-induced tsunamis using a low-dissipation Riemann weakly compressible smoothed particle hydrodynamics model

In order to address the complex moving boundary problem and violent free-surface flows involved in the process of landslide tsunamis, this paper proposes a new low-dissipation Riemann-solver-based smoothed particle hydrodynamics (SPH) numerical model. In this model, the complex fluid-rigid slide interaction and friction effects on landslides are handled by an improved motion boundary approach based on a one-sided Riemann scheme. A weighted essentially non-oscillatory (WENO) reconstruction method is introduced to reduce excessive dissipation of the fluid caused by rigid landslide impact. The kernel gradient accuracy is assured by applying the corrected smoothed particle method (CSPM) into a continuity equation. Single-block and multi-block landslide tsunamis in two-dimensional (2D) or three-dimensional (3D) applications are simulated with different particle resolutions, and the results are compared to those of other numerical models and experimental results. The landslide trajectories obtained from the modified boundary model are in good agreements with the experimental values. Free-surface flows variations during the propagation are properly captured in the current simulations without introducing any artificial viscosity term, and the wave peaks exhibit deviations of −14%–10.5% from the experimental values, thus verifying the usability and robustness of the proposed Riemann-SPH model.

Spatial landslide risk assessment in a highly populated Rohingya refugee settlement area of Cox’s Bazar, Bangladesh

ABSTRACT In recent times, landslides have become a major concern in the southeast part of Bangladesh. This study aims to develop comprehensive landslide risk mapping by applying the analytical hierarchy process (AHP) and geospatial techniques in Ukhiya and Teknaf Upazilas, a highly populated Rohingya Refugee Settlement area. To assess the landslide risk, we selected 12 influencing criteria of hazard, vulnerability and exposure under the relevant components of risk, such as precipitation intensity, landslide inventory, distance to fault line, stream density, distance to stream network, elevation, aspect, slope, geology, normalized difference vegetation index (NDVI), land use and land cover (LULC), and population density. These spatial criteria were weighted using the AHP model. The weighted overlay techniques were then used to produce the landslide risk map. The findings demonstrate that 2.19% of the total area is classified as a very high risk zone, and 12.74% is categorized as a high risk zone. Moderate risk areas cover 23.08% of the total area. The risk map is validated by the landslides inventory. The outcomes can be used by the concerned authorities to take the necessary steps to reduce the impact of landslides.

Impact of landslide on geoheritage: Opportunities through integration, geomorphological classification and machine learning

Landslides are widely understood to cause damage to the geological features and the surrounding environment. Our study focuses on the northern region of the Karangsambung-Karangbolong Geopark (KKNG), characterized by diverse lithology and multi-phase tectonics. This study aims to explore (i) landslide susceptibility assessment, (ii) geomorphological characteristics and distribution of landslide susceptibility, and (iii) identification of landslide impacts on geosites. We mapped morphogenesis, morphology, materials, and processes to understand the geomorphological context, identifying three primary landforms: structural, pediments, and fluvial. For landslide susceptibility mapping, we used the XGBoost algorithm with cross-validation and utilized the area under the receiver operating characteristic curve (AUROC) for model validation. The XGBoost model revealed a high susceptibility classification for 10 geosite points. Landslides have negative impacts, such as Olistoliths of coral limestones, Exotic-blocks of chert, and calcareous red claystone that change landforms and damage outcrops. Nevertheless, some landslides have positive impacts on the geosite, such as Exotic-blocks of phyllites, and Exotic-blocks of pillow lava and radiolarian chert, because landslides can reveal fresher outcrops and rock structures, and the outcrop area becomes larger. Landslide mapping has successfully identified geosites that are highly vulnerable and have adverse impacts, especially those with certain lithological characteristics. This research on viewing disaster as a harmful process has evolved into a more holistic view of the disaster. This view includes various positive aspects that involve understanding the complex interactions between geology and geomorphology towards the geosite. By understanding the relationship between geomorphologic features (morphology, material, process, morpho-arrangement) and landslide occurrence, effective management strategies can be implemented to develop geological heritage further.

Fracture mechanics analysis of gas pipeline with circumferential crack under the action of transverse landslide

The whole process of pipeline–soil interactions between a landslide and a gas pipeline was simulated using the strength reduction method. The main purpose is to investigate how pipeline internal pressure, landslide displacement, crack depth ratio (the ratio of crack depth to wall thickness), crack aspect ratio (the ratio of crack depth to crack half-length), and reinforcement with anti-slide piles influence the J-integral of a circumferential crack in the pipeline under landslide impact. The simulation showed that increases in the landslide displacement, crack depth ratio, and pipeline internal pressure led to an increase in the maximum value of the J-integral at the crack leading edge. An increase in the crack aspect ratio reduced the maximum value of the J-integral. In addition, in the process in which the crack shape changed from semi-elliptical to semicircular, there was a critical crack aspect ratio at which the J-integral at the deepest point of the crack was equal to the J-integral at a point on the surface. The study also found that an increase in the number of anti-slide piles affected the location and distribution of the zone of maximum slippage and effectively reduced the maximum von mises stress and the J-integral at the crack tip. However, when the number and cross-sectional size of anti-slide piles exceed a certain threshold, further increasing or enlarging them barely affects the J-integral.

Anticipating the impact of glaciers, landslides and extreme weather events on vulnerable hydropower projects and the development of an integrated multi-hazard warning system (IMWS)

The Himalayan river basins offer great potential for hydropower development, but they are also vulnerable to various hazards such as debris flows, landslides, flash floods, glacial lake outburst floods (GLOFs), and landslide lake outburst floods (LLOFs). Despite the regional and global significance of these hazards, there is a lack of information and data on different aspects, including meteorology, hydrology, geology, and seismology. Many hazards often go unnoticed or receive little attention until they start affecting humans and their activities like damage to buildings, infrastructure, and other human-made structures. It is important to recognize that hazards can have severe and long-lasting impacts on society, even when they do not directly affect humans. For example, flash floods can disrupt ecosystems, destroy habitats, and threaten biodiversity and the complexity of climatic influences on both regional and local scales cannot be overlooked. Therefore, we highlight the importance of basin-wise and basin-wide continuous long-term monitoring in the Himalaya. It is also recommended that the highest hydropower projects in the basins should have their network of hydro-meteorological observatories at different altitudes with a provision of real-time data transmission and deployment of a multi-hazard warning system (IMWS) for flash floods.

Observations and computational multi‐phase modelling in tropical river settings show complex channel changes downstream from rainfall‐triggered landslides

AbstractAlluvial river channels respond to changes in sediment supply by adjusting their geometry. Landslide sediment delivery and geomorphic response of river channels during floods are poorly understood and rarely examined in tropical settings. We investigate the impact of landslides on channel geomorphic changes during an extreme typhoon‐induced flood event in the Philippines, specifically the complex geomorphic response of the Antamok River to Typhoon Mangkhut in September 2018, which triggered >500 landslides in the Ambalanga catchment. The catchment has a legacy of anthropogenic modifications, such as extensive small‐scale (artisanal) mining and tailings storage facilities (TSFs) from large‐scale mining activities.We use a novel mix of mapping and computational modelling approaches to test the hypothesis that landslide sediment delivery is a major control on channel geomorphic change. Pre‐ and post‐event imagery show that the overall active channel area increased by 35.9% and the mean active channel width increased by 9.1 m. Spatially, we find no clear relationship between landslide sediment input or unit stream power and channel width geomorphic change, with longitudinal changes in active channel width complicated by TSFs. Multi‐phase modelling using r.avaflow revealed how landslide sediment delivery and TSFs interacted with the flow to generate the observed patterns of channel change. The model simulated channel incision in the upper parts of the catchment (up to 0.78 m) and deposition in the TSFs (up to 1.73 m).Our findings demonstrate that well‐established methods (e.g., stream power threshold) fail to fully explain channel width geomorphic changes, particularly for anthropogenically altered catchments. Integrating techniques, such as landslide mapping and multi‐phase computational modelling improves understanding of sediment supply's role in channel width change during extreme events. Numerical simulations also demonstrate that conventional assumptions of increased erosion and deposition with rising flow discharge are inaccurate with large sediment input, highlighting instead the effectiveness of multi‐phase models.

Detection of long-term slope displacement using time-series DInSAR and geological factor analysis for susceptibility assessment of landslides in northwestern Kyushu Island

Ongoing climate change has increased the impact of landslides and related slope disasters on infrastructure and human lives. Microwave satellite remote sensing, particularly interferometric analysis of synthetic aperture radar (InSAR) data, is a powerful tool for routine monitoring of the displacement of slopes on small wavelength scales, independently from solar illumination and cloud coverage. Although various sophisticated techniques for time-series InSAR have been developed, practical application in conjunction with geological analysis to reveal intrinsic and triggering factors of slope displacement is still limited. In this paper, we describe a practical case of time-series InSAR analysis with a special focus on its geological implications in northwestern Kyushu, a high-risk area for slope-related disasters in Japan. The extracted susceptible polygons from the InSAR results show accuracy comparable with that of other recent research on landslide mapping. Intrinsic factor analysis based on a geographic information system reveals that the displacement occurs on relatively gentle slopes instead of steep areas, corresponding to the transient zone of Paleogene-Neogene sedimentary rocks and basalt. Triggering factor analysis based on correlation coefficients reveals a significant link between some displacement areas with mean and/or maximum precipitation for each observation duration. Those findings confirm the importance of carefully accounting for the geological background for landslide susceptibility assessment and policy making, apart from topographic and meteorological conditions.

Assessment of factors causing landslides using the Analytical Hierarchy Process (AHP) method

Landslides are Indonesia's second most common disaster in the last ten years. Landslides cause losses, reaching hundreds of billions and threatening human safety. For this reason, it is necessary to take action to reduce the negative impact of landslides. Physical and non-physical mitigation can occur before, during, and after a disaster. The most essential thing in disaster mitigation is knowing the major factors that cause landslides. This research aims to analyze the major and minor factors that cause landslides using the Analytical Hierarchy Process (AHP) and the Likert scale. Respondents in this research are experts in disasters, especially landslides—respondents from universities in Indonesia, practitioners, and people involved in disaster mitigation. Validity and reliability tests were carried out on each question used in the questionnaire. The research results show that the landslide disaster was caused by a disruption in balance that occurred due to internal and external disturbance factors. The major external factors that cause slope failures are the cutting of hills and changes in land use to built-up areas. In contrast, the major internal factors that trigger landslides are heavy rainfall and steep topographic conditions. The results of this research in the form of weight and scoring values for each factor causing landslides can be used as a reference for mapping landslide areas to identify areas with the potential for landslides.