Landslide Sites Research Articles

Development of Potential Slip Surface Identification Model for Active Deep-Seated Landslide Sites: A Case Study in Taiwan

Identifying locations of landslide slip surfaces provides critical information for understanding the volume of landslides and the scale of disasters, both of which are essential for formulating disaster preparedness and mitigation strategies. Based on hydrogeological survey data from 24 deep-seated landslide-prone sites in Taiwan’s mountainous regions, this study developed the hydraulic conductivity potential index (HCPI) using principal component analysis to quantify the hydraulic properties of disturbed rock formations with six geological factors. Then, regression analysis was performed to construct a permeability estimation model for the geological environment of landslides. Finally, the established model was utilized to develop an identification method for potential slip depths in landslide-prone sites. Results indicated a strong relation between HCPI and hydraulic conductivity with a determination coefficient of 0.895. The relation equation confirmed that the data it generated concerning the depths of significant changes in hydraulic conductivity could be used to identify potential slip surfaces. Additionally, this study successfully established a rule for identifying potential slip zones by summarizing data concerning the generated hydraulic conductivity profiles, stratigraphic lithology, existing inclinometer slip depth records, and groundwater level of landslide sites. This identification method was then applied to predict potential slip depths for ten landslide sites where slip surfaces have not yet occurred. These findings offer a new alternative to having early information on potential sliding depths for timely disaster management and control implementation.

Investigation of landslide triggers on Mount Oku, Cameroon, using Newmark displacement and cluster analysis

BackgroundThe landslide inventory of the western flank of Mount Oku, Cameroon, includes spreads or complex landslides, indicating sudden soil weakening, possibly due to seismic activity or heavy rainfall causing groundwater rise. These landslides were likely triggered between 2009 and 2018 based on the dates of the aerial imagery. Identifying triggers for past landslides remains a major unresolved issue in landslide science. However, understanding these triggers is crucial for accurately assessing future landslide hazards.MethodologyIn this paper, we investigate the possibility of earthquakes to precondition landslide development or reactivation during climatic events. By assuming a magnitude 5.2 earthquake, an epicenter of 10 km from this area, and different wetness conditions, the factor of safety (FS) and Newmark displacement (ND) models were calculated for shallow and deep-seated landslides with sliding depths of 3 and 7.5 m. Afterward, the relationship between FS, assumed ND, and observed landslides was analyzed in a cluster analysis, to derive patterns of climatically and seismically triggered landslides.ResultsThe comparison of FS maps and FS values of the observed landslides revealed that especially for landslides at 7.5 m depth, most sites that are stable during dry conditions become instable under saturated conditions, indicating a climatic trigger. At 3 m depth, however, some landslide sites that are still marginally stable under saturated conditions, display relatively high ND values for the investigated hypothetical earthquake, indicating a possible seismic influence. In the cluster analysis, we clustered the observed landslides according to their distances to rivers and topographic ridges and obtained three clusters. Landslides from cluster 3 with 31% of the landslides display medium to high ND for the assumed earthquake, and were found near ridges and farther away from rivers, suggesting seismic triggering. Cluster 2, with 12% of landslides closer to rivers, suggested climatic origins. Thus, while climate is a critical landslide contributing factor, seismic events may also contribute, either by predisposing to landslides or by reactivating them alongside climatic factors. These results enable the establishment of more precise and effective landslide mitigating measures considering mostly rainfall but also earthquakes as possible triggers.

Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.

The Evolution of Instability at the A83 Rest and be Thankful from 2008 to 2023

High resolution panoramic imagery has been systematically captured at the A83 Rest and be Thankful landslide site in Scotland since 2016. This paper describes the derivation of quantitative data from the imagery from two of the stations from which panoramic imagery has been captured. The high-resolution panoramic imagery has been supplemented by earlier lower resolution panoramic and non-panoramic imagery where available to extend the time period that the data covers. The data capture process involves measuring reference areas and areas of apparent instability on each image in the time series. Normalised areas affected are then plotted over time as percentages. The data presented here provide significant and valuable insight into the extent and temporal change in instability on the hillside over the past 15 years and 11 years for the northern and southern parts of the slope, respectively. The data represent percentage areas of apparent instability and show relative change over time. The changes measured and reported confirm other observations that the extent of instability within the last decade has increased significantly and the panoramic imagery monitoring forms an integral part of the overall monitoring and inspection strategy.

Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological surveys due to its low-cost, ease of use, and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide, we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a newly developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views, we can quickly detect the occurrence of collapse events, determine their location, and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we conducted preliminary tests of the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining the collapse information proposed in this paper can extend the potential of landslide monitoring systems based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.

Utilizing multi-criteria decision-making analysis and 3D visualization techniques for dam site selection and irrigation area identification in Gedeb River, Ethiopia

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

Changes in vegetation composition and structure following landslide-induced disturbance in the Himalaya

The Himalaya cover 12% of India's landmass and are prone to approximately one-seventh of global rainfall-triggered landslides. Still, very few studies have examined the after-effects of landslides on native vegetation structure and composition. This study aims to fulfill this gap by analyzing the vegetation structure and composition of 10 landslide-impacted sites in Uttarakhand Himalaya along an elevational gradient between 1400–3500 m. The investigations revealed that physiognomically, the younger landslide-disturbed sites were dominated by herbaceous taxa while shrubs and trees dominated the older landslide-disturbed sites. Shannon–Wiener diversity values were highest at the old-low disturbed site compared with recent and young disturbed sites. Sorensen similarity index values indicated that the older landslide sites had the highest similarity in species composition of disturbed and undisturbed sites. The younger and recently disturbed landslide sites were highly dissimilar in species composition and structure as compared to the adjacent undisturbed sites. Notably, both the landslide-disturbed and undisturbed sites had a high percentage of native species (90%–95%). The fundamental understanding developed from this study can have potential applications in evolving management practices for ecological restoration of the degraded ecosystems in the Himalaya and other mountain ecosystems around the world.

Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

Assessment of hydropedological characteristics at medium-sized landslide sites in Manafwa catchment, Mount Elgon, Uganda

ABSTRACT Though strongly linked to landslides, hydropedological characteristics are inadequately investigated in Manafwa catchment. This study investigated hydropedological characteristics at medium landslide sites in Manafwa catchment. The design included 12 infiltration experiments that were conducted at each site. To determine the selected soil physical properties, composite soil samples were extracted from the infiltration sites for soil laboratory analysis. Soil cores were extracted to determine saturated hydraulic conductivity (Ksat) and bulk density. The observed high infiltration rates and rapid Ksat underscore the susceptibility of the studied sites to landslide hazards. Additionally, soil organic matter, clay content, and bulk density further contribute to the understanding of the hydropedological factors influencing landslide occurrences. These findings reveal the need for comprehensive investigations into hydropedological characteristics of other landslide categories to assist in landslide risk assessment and management strategies.

Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China.

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.

Mechanism of a rainfall-induced landslide in a large-scale flume experiment on a weathered granite sand

IntroductionsA large-scale flume experiment was performed to evaluate the mechanism of landslide occurrence due to rainfall using weathered granite sand. The dimensions of the flume were 9 m (length), 1 m (width), and 1 m (depth). The weathered granite sand from the actual landslide site at Da Nang City, Vietnam was used. The pore water pressure was measured by a pore-water pressure transducer at two depths (middle and bottom) to determine the process of rainwater infiltration into the soil. The surface deformation was measured with extensometers at three positions of the slope. The deformation of the entire slope was determined by the 160 cylindrical-shaped makers evenly spaced in the slope and three cameras.ResultsThe results showed that the rainfall infiltrated into the slope process, increasing from negative pore water pressure to approximately 0. The maximum shear strain contour has been plotted in total and in time increments. The shear band was detected from the time increments maximum shear strain contour. The localization in the shear band formed just before failure.ConclusionsTo the best of our knowledge, this is the largest scale laboratory test ever conducted to calculate the shear band. Moreover, it was found that the failure occurred when the sand was in an unsaturated phase. Failure does not seem to depend on the increase in pore water pressure but on the maximum shear strain. This feature can be used to explain the phenomenon of landslides that occur even when the groundwater level does not increase but large deformation occurs.

Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m3/m3 saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

Líĺwat Climbers Could See the Ocean from the Peak of Qẃelqẃelústen: Evaluating Oral Traditions with Viewshed Analyses from the Mount Meager Volcanic Complex Prior to Its 2360 BP Eruption

Abstract Among Líĺwat people of the Interior Plateau of British Columbia, an oral tradition relays how early ancestors used to ascend Qẃelqẃelústen, or Mount Meager. The account maintains that those climbers could see the ocean, which is not the case today, because the mountain is surrounded by many other high peaks, and the Strait of Georgia is several mountain ridges to the west. However, the mountain is an active and volatile volcano, which last erupted circa 2360 cal BP. It is also the site of the largest landslide in Canadian history, which occurred in 2010. Given that it had been a high, glacier-capped mountain throughout the Holocene, much like other volcanoes along the coastal range, we surmise that a climber may have reasonably been afforded a view of the ocean from its prior heights. We conducted viewshed analyses of the potential mountain height prior to its eruption and determined that one could indeed view the ocean if the mountain were at least 950 m higher than it is today. This aligns with the oral tradition, indicating that it may be over 2,400 years old, and plausibly in the range of 4,000 to 9,000 years old when the mountain may have been at such a height.

Uncertainty reduction strategies to enhance geotechnical site characterization: a case study of the red roof landslide in Wyoming

This study presents a method for identifying strategic locations to drill additional boreholes by quantifying and reducing subsurface uncertainties in geotechnical site investigations. The case study is the Red Roof landslide site located near milepost 140 on US Highway 26/89 in Teton County, Wyoming. A landslide remediation report had recommended additional boreholes before completion of the project. Two primary sources of uncertainty in geomaterials, namely geological and ground, are evaluated to determine the locations for these additional boreholes. The study aims to enhance site characterization and improve the accuracy of geotechnical assessments by strategically selecting and drilling additional boreholes that will reduce these subsurface uncertainties. The method involves reviewing and collecting the available site investigation data. An extensive geostatistical simulation is conducted based on the available data to identify spatial locations at the site that have uncertain subsurface conditions. Data from the additional boreholes could be utilized in these areas to reduce the uncertainty. This approach aligns with the current multi-phasing of site investigation in engineering practice, where a preliminary investigation is conducted before a detailed investigation. The results show that the locations of additional boreholes identified using this method differ from the ones earlier recommended, showcasing the effectiveness of the methodology in this application. This study also provides insights into effective strategies for reducing uncertainties through strategic borehole placement in similar geotechnical investigations. By quantifying geological and ground uncertainties, the method enables informed decision-making for slope stability analysis and risk assessment, with implications for infrastructure stability and geohazard mitigation.

Is the Tupul (Makhuam) Landslide of 29-30 June 2022 in Manipur, India Recognized as one of the Rainfall Induced Massive Landslide?

The present study deals with Tupul landslide in Manipur which triggered on the intervening night of 29th -30th June 2022 in Noney district of Manipur. The death toll was 61 persons in total from Territorial Army, NF Railway’s employees, Villagers and labours from various contractors in the construction of railway line from Jiribam to Imphal. We have conducted a drone survey on the landslide site for preparation of large-scale terrain map and compared 2009 CARTOSAT image to pick up the slip surfaces. The detailed fieldwork of the study area deciphers highly jointed, fractured, faulted rock formations and display neo-tectonic movements. The slope failure analysis was conducted using the Phase-2 software and the failure was compared with the Drone made DEM, which is used to estimate the volume of debris falling on the railway formations. The excessive precipitation (375.6mm) during the May 2022 was recorded near the landslide site which was 60% more than the normal precipitation. In order to maintain the safety of the man and materials for the future, a number of suggestions were proposed for future monitoring of slope along coming up railway lines of Northeast Frontier Railways.

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

The Mechanism of Mineral Dissolution on the Development of Red-Bed Landslides in the Wudongde Reservoir Region

The Wudongde reservoir region exhibits a notable prevalence of landslides within the red-bed reservoir stratum. The red bed is a clastic sedimentary rock layer dominated by red continental deposits. It is mainly composed of sandstone, mudstone, and siltstone. The lithology is diverse and uneven. In this study, we delve into the impact of mineral dissolution on the development of red-bed landslides in the reservoir area by utilizing the Xiaochatou landslide as a representative case study. Considering the inherent susceptibility of red-bed formations to erosion, collapse, and softening when exposed to water, an investigation was conducted to examine the consequences of mineral dissolution on landslides occurring in these areas. We conducted a mineral analysis and an identification of rock samples from the Xiaochatou landslide site, revealing alternating layers of sandstone and mudstone. Sandstone and conglomerate specimens were immersed in deionized water, and advanced techniques such as scanning electron microscopy (SEM), ion chromatography (IC), and inductively coupled plasma (ICP) analysis were used to examine the effects of water immersion. We also employed the hydrogeochemical simulation software PHREEQC to understand the dissolution mechanism of gypsum during soaking. Our findings reveal that sandstone and conglomerates harbor a notable quantity of gypsum, which readily dissolves in water. Prolonged immersion leads to erosion cavities within the sandstone, thereby augmenting its permeability. The concentration of SO42− ions in the soaking solution emerges as the highest, followed by Ca2+ and Na+. The notable significance is the dissolution of gypsum, whose intricate mechanism is contingent upon diverse environmental conditions. Variations in ion concentration profoundly influence the saturation index (SI) value, with the pH value playing a crucial role in shifting the reaction equilibrium. Regarding the deformation mode of the landslide, it manifests as a combination of sliding compression and tension cracking. The fracture surface of the landslide assumes a step-like configuration. As the deformation progresses, the mudstone layer takes control over the sliding process, causing the sandstone to develop internal narrow-top and wide-bottom cracks, which propagate upward until the stability of the slope rock mass is compromised, resulting in its rupture. In this manuscript, we delve into the dissolution traits of red-bed soft rock in the Wudongde reservoir area, using a landslide case as a reference. We simulate this rock’s dissolution under environmental water influences, examining its interaction with diverse water types through rigorous experiments and simulations. This study’s importance lies in its potential to shed light on the crucial engineering characteristics of red-bed soft rock.

Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area’s disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation’s findings mostly agreed with the survey’s actual findings. The study’s findings demonstrated that: (1) the FCNN model’s AUC value was 0.910, while the I-AHP model’s AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened.

Study on wavelet multi-scale analysis and prediction of landslide groundwater

Abstract Current groundwater prediction models often exhibit low accuracy and complex parameter adjustment. To tackle these limitations, a novel prediction model, called improved Aquila optimizer bi-directional long-term and short-term memory (IAO-BiLSTM) network, is proposed. IAO-BiLSTM optimizes the hyperparameters of the BiLSTM network using an IAO algorithm. IAO incorporates three novel enhancements, including population initialization, population updating, and global best individual updating, to overcome the drawbacks of current optimization algorithms. Before making predictions, the challenge posed by the highly nonlinear and non-stationary characteristics of groundwater level signals was addressed through the application of a wavelet multi-scale analysis method. Using a landslide site in Zhejiang Province as an example, a monitoring system is established, and continuous wavelet transform, cross-wavelet transform, and wavelet coherence analysis are employed to perform multi-scale feature analysis on a 2-year dataset of rainfall and groundwater depth. The findings reveal that the groundwater depth of monitoring holes exhibits similar high energy resonating periods and phase relationships, strongly correlating with rainfall. Subsequently, IAO-BiLSTM is employed to predict groundwater depth, and its results are compared with seven popular machine learning regression models. The results demonstrate that IAO-BiLSTM achieves the highest accuracy, as evidenced by its root mean squared error of 0.25.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.