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Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features. [ABSTRACT FROM AUTHOR]

Gully erosion is the most intensive type of water erosion and it leads to land degradation across the world. Therefore, analyzing the spatial occurrence of this phenomenon is crucial for land management. The objective of this research was to predict gully erosion susceptibility in the Kakia-Esamburmbur catchment in Narok, Kenya, which is badly affected by gully erosion. GIS and ensemble techniques using weight of evidence (WoE) and logistic regression (LR) models were used to map the susceptibility to gully erosion. First, 130 gullies were detected in the study area and portioned out 70:30 for training and validation, respectively. Nine gully erosion conditioning factors were selected as predictors. The relationships between the gully locations and the factors were identified and quantified using WoE, LR and WoE–LR ensemble models. The results show that land use/cover, distance to road, sediment transport index (STI) and topographic wetness index (TWI) are the factors that have the most influence on gully occurrence in the catchment. Additionally, the WoE–LR model performed better than the WoE and LR models, producing an AUC value of 0.88, which was higher than that of the WoE model, 0.62 and the LR model, 0.63. Therefore, the WoE–LR ensemble model is useful in gully erosion susceptibility mapping and is of help to decision makers in land-use planning. [ABSTRACT FROM AUTHOR]

Landslides are important agents of sediment transport, cause hazards and are key agents for the evolution of scarplands. Scarplands are characterized by high-strength layers overlying low-inclined landslide-susceptible layers that precondition and prepare landsliding on geological timescales. These landslides can be reactivated, and their role in past hillslope evolution affected geomorphometry and material properties that set the framework for present-day shallow landslide activity. To manage present-day landslide hazards in scarplands, a combined assessment of deep-seated and shallow landsliding is required to quantify the interaction between geological conditions and vegetation that controls landslide activity. For this purpose, we investigated three hillslopes affected by landsliding in the Franconian scarplands. We used geomorphic mapping to identify landforms indicating landslide activity, electrical resistivity to identify shear plane location and a mechanical stability model to assess the stability of deep-seated landslides. Furthermore, we mapped tree distribution and quantified root area ratio and root tensile strength to assess the influence of vegetation on shallow landsliding. Our results show that deep-seated landslides incorporate rotational and translational movement and suggest that sliding occurs along a geologic boundary between permeable Rhätolias sandstone and impermeable Feuerletten clays. Despite low hillslope angles, landslides could be reactivated when high pore pressures develop along low-permeability layers. In contrast, shallow landsliding is controlled by vegetation. Our results show that rooted area is more important than species-dependent root tensile strength and basal root cohesion is limited to the upper 0.5 m of the surface due to geologically controlled unfavourable soil conditions. Due to low slope inclination, root cohesion can stabilize landslide toes or slopes undercut by forest roads, independent of potential soil cohesion, when tree density is sufficient dense to provide lateral root cohesion. In summary, geology preconditions and prepares deep-seated landslides in scarplands, which sets the framework of vegetation-controlled shallow landslide activity. [ABSTRACT FROM AUTHOR]

Regarding evaluating disaster risks in Iran's West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP's accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP. [ABSTRACT FROM AUTHOR]

Geomorphological knowledge is critical in understanding watershed scale surface processes, including steep mountainous areas and flat lowlands, particularly if the mid- and downstream areas are densely populated and hazard assessments are highly required. However, our knowledge about such surface processes has relatively been limited in some areas in South Asia due likely to the lack of comprehensive studies of geomorphology and related fields. This article undertakes an overview of the geomorphological processes of the disaster-prone deltaic landscape of the Ganges–Brahmaputra–Meghna (GBM), particularly focusing on fluvial processes. The area locates in the downstream of the watershed system including Himalayan Mountains and highly connected with the upper basin morphodynamics, hydrology, and sediment flux. The previous studies are summarized at different geomorphic settings concerning hillslopes, fluvial plains, and coastal areas to provide clarity about the geomorphic processes linking erosion-prone upstream source areas to deposition-dominated downstream areas. The review found that most of the geomorphic researches in Bangladesh are exploring landslide inventory and susceptibility mapping in hilly areas; river channel or riverbank shifting, riverbank erosion and accretion in fluvial environments; watershed morphometric analysis and geomorphic unit identification in plain land; and coastline shifting or coastal erosion and accretion in coastal environments at a small scale. Then, we discuss the fluvial dynamics and sediment transport of the GBM river system to address the knowledge gap in the context of deltaic plain land in Bangladesh, where upstream fluvial sedimentation processes impact the geomorphic connectivity from Himalayan to the Bay of Bengal. Although some studies on the fluvial dynamics and sediment dispersal in the upstream GBM river basin are present, the fluvial processes in the downstream domain of Bangladesh are not fully understood with a limited number of research with field-based approaches. Some future perspectives of geomorphic research in Bangladesh are then mentioned to understand better the complex geomorphological settings in the entire GBM watershed and to strengthen the existing research capacity. This review will also develop a holistic understanding of fluvial geomorphic processes of the GBM River to the policymakers and may be helpful to improve the transboundary river basin management policies or strategies. [ABSTRACT FROM AUTHOR]

A mega-earthquake in a mountainous region can trigger thousands of landslides. Subsequently, these loose co-seismic materials will experience the erosion-transport-sediment process. This process is closely associated with a series of cascading hazards, such as landslides, debris flows and floods. The disturbance and legacy effects of earthquakes have become the focus of attention for post-earthquake reconstruction and disaster mitigation. By combining the modeling of seismic-induced landslide magnitude with an analysis of the coseismic material sediment connectivity and transfer processes, we assessed the impacts of the 2008 Wenchuan earthquake on the mass balance and subsequent geohazard activities. The 2008 Wenchuan earthquake produced 22,785 Mt of deposit storage, 37.9 % of which (∼8636 Mt) is connected to the channels and prone to erosion and transfer by hydrodynamic forces, and the remaining 62.1 % (∼14,149 Mt) was deposited on hillslopes far from the channel network and contributed little to the sediment dynamics. Long-term field observations found a causal relationship between decreasing surface erosion rate and gradual healing of co-seismic deposits due to coarsening by progressive removal of fine grains. We evaluated the residence time of fine-grained (<0.25 mm) landslide materials using the post-earthquake sediment evacuation rate and identified three stages regarding the enhanced erosion and surface mass wasting after the 2008 Wenchuan earthquake, namely, the high activity period (within 40.6 ± 5.4 years), the low activity period (40.6 ± 5.4–107.1 ± 14.2 years), and the steady period (after 107.1 ± 14.2 years). This has important implications for the hazard assessment of landslides and debris flows in understanding and modeling the post-seismic geohazard evolution over multiple timescales. • We explored the control of geological and environmental factors on the magnitude of co-seismic landslides. • A modelling of capturing seismic-shaking patterns derived from earthquake-induced landslides was developed. • We explained how the landslide-channel connectivity regulates post-seismic sediment transport. • we quantified the timescale of the enhanced surface mass wasting related to the 2008 Wenchuan earthquake. [ABSTRACT FROM AUTHOR]

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS-BBO and ANFIS-ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo-environmental characteristics for taking measures to manage and prevent flood damages. Image 1 • Prediction power of two novel ensemble methods for flood susceptibility mapping. • The proposed ensemble methods can improve the prediction performance of ANFIS. • The proposed methods can accurately produce flood susceptibility maps for mitigation and management. [ABSTRACT FROM AUTHOR]

Tectonic and geomorphic processes drive landscape evolution over different spatial and temporal scales. In mountainous environments, river incision sets the pace of landscape evolution, and hillslopes respond to channel incision by, e.g., gully retreat, bank erosion, and landslides. Sediment produced during stochastic landslide events leads to mobilization of soil and regolith on the slopes that can later be transported by gravity and water to the river network during phases of hillslope–channel geomorphic coupling. The mechanisms and scales of sediment connectivity mitigate the propagation of sediment pulses throughout the landscape and eventually drive the contribution of landslides to the overall sediment budget of mountainous catchments. However, to constrain the timing of the sediment cascade, the inherent stochastic nature of sediment and transport through landsliding requires an integrated approach accounting for different space scales and timescales. In this paper, we examine the sediment production on hillslopes and evacuation to the river network of one landslide, i.e. the Schimbrig earthflow, affecting the Entle River catchment located in the foothills of the Central Swiss Alps. We quantified sediment fluxes over annual, decadal, and millennial timescales using respectively unmanned aerial vehicle (UAV)–structure-from-motion (SfM) techniques, classic photogrammetry, and in situ produced cosmogenic radionuclides. At the decadal scale, sediment fluxes quantified for the period 1962–1998 are highly variable and are not directly linked to the intensity of sediment redistribution on the hillslope. At the millennial scale, landslide occurrence perturbs the regional positive linear relationship between sediment fluxes and downstream distance as the landslide-affected Schimbrig catchment is characterized by a decrease in sediment fluxes and a strong variability. Importantly, the average decadal sediment flux of the Schimbrig catchment is 2 orders of magnitude higher than millennial sediment fluxes computed over the same spatial extent. The discrepancy between decadal and millennial sediment fluxes, combined to the highly variable annual sediment evacuation from the hillslopes to the channel network suggest that phases of hillslope–channel geomorphic coupling are short and intermittent. During most of the time, the first-order catchments are transport-limited and sediment dynamics in the headwaters are uncoupled from the fluvial systems. In addition, our unique spatio-temporal database of sediment fluxes highlights the transient character of the intense geomorphic activity of the Schimbrig catchment in a regional context. Its decadal sediment flux is of the same order of magnitude as the background sediment flux going out of the entire Entle River catchment. Over the last 50 years, the Schimbrig catchment, which represents ca. 1 % of the entire study area, provides 65 % of the sediments that the entire Entle catchment will supply over the millennial scale. These results suggest that episodic supply of sediment from landslides during intermittent phases of hillslope–channel geomorphic coupling are averaged out when considering sediment fluxes at longer timescales and larger spatial scales. [ABSTRACT FROM AUTHOR]

After the 2008 Mw 7.9 Wenchuan earthquake, geological hazards occurred frequently in the southwest mountainous watershed. Frequent landslide disasters provide abundant sediment supply for mountain torrent disasters. The estimation of the potential landslide volume is essential for the risk assessment of mountain torrent disasters. In this study, a method of calculation that combines TRIGRS and the slope-units for estimating the landslide volume of a small mountainous watershed has been established. TRIGRS analyzes the watershed landslide safety factor under rainfall conditions based on grid-cells. The slope-units extract the results and combine the empirical power law formula to calculate the potential landslide volume. In this paper, we use this method to assess the landslide volume of the Longxi river basin. The results show that the area and volume estimates of the landslides are consistent with the results observed from satellite images and field surveys. This method can be used to study the impact of sediment transport on mountain torrent disasters in the basin. With different moisture content conditions, the results show that the soil moisture content and slope angle significantly affect the distribution and volume of potential landslides in the watershed, giving rise to the uncertainty of the landslide estimation. [ABSTRACT FROM AUTHOR]

Landslides have long been considered one of the main erosive drivers that sculpt hillslopes over time. Tectonic forces raise slopes that can be sculpted by climate and the degree to which they can be sculpted depends upon the underlying lithology and hillslope geomorphology. Landslides destroy life and property and contribute to the evolution of mountain landscapes by generating and transporting hillslope sediments. In this study, we investigate the dynamics of soil-mantled hillslopes in eastern Oklahoma and western Arkansas, USA, by mapping shallow landslides, evaluating the frequency and magnitude of the landslides, and quantifying the surface geometries. We mapped 185 shallow landslides in a 150 km2 area. The approximate ages of landslides and the frequencies and magnitudes (area, volume and runout) were estimated using historical aerial photographs. The landslide database was then used to compute rate of landslide occurrence and sediment yield. In addition, landslide surface geometries, including slope, curvature and surface roughness, were determined from 10 m NED and 2 m LiDAR elevation data. The hillslope susceptibility studies, derived from elevation data, were augmented by our review of common historical accounts that link landslides in the study region to the 1952 M 5.4 El Reno, Oklahoma earthquake ~300 km away. In light of that case study, we determined the frequency of historical earthquakes that impart >2 kPa dynamic stresses for the study area since 1900 and find an increased potential for dynamic stress perturbation of landslide activity since ~2010. All these characteristics were then evaluated to understand the dynamics of hillslopes and hillslope susceptibility to shallow landslides. Frequency assessment of mapped landslides suggested that frequency of landslides increased significantly during 2005–2016 compared to 1995–2005. On average ~10 shallow landslides occurred every year during 2005–2016. These landslides alone contributed ~1.5 × 105 m3/yr (sediment yield rate: ~0.10 cm/yr) of sediments. Surface roughness values were relatively higher in landslide areas compared to that of surrounding non-landslide areas. The surface roughness index mapped ~84% of observed landslides, and suggests that the index could be used to map hillslope susceptibility to landslides for future planned studies. [ABSTRACT FROM AUTHOR]

Few models are capable of simultaneously simulating the sequences of landslide occurrence and sediment export. Quantification of the temporarily stored sediment within the watershed plays a key role to link hillslope landslides with fluvial sediment export. In this study, two coupled models were proposed to simulate time-series total landslide area and the subsequent sediment export on a daily basis with only the inputs of rainfall and runoff. The landslide model considers per-existing and models new landslide, and the sediment transport model incorporates a sediment storage variable. The landslide and sediment transport model were well evaluated with Nash-Sutcliffe efficiency (EC) of 0.89 and logarithmic Nash-Sutcliffe efficiency (EClog) of 0.90, respectively, in the Tsengwen Reservoir watershed in southern Taiwan by using long-term observed data (2005–2015). It is found that reactivated landslides were up to 72% of the pre-landslide area, which contributed sediment comparable to the new landslide. Besides, the landslide model indicates that pre-landslide area controls the total landslide area but when rainfall is large it takes control in turn. With the simulation of sediment storage, the sediment transport model can well simulate the sediment export after the catastrophic event (typhoon Morakot in 2009). During the post-Morakot period, small rainfall and runoff can lead to high sediment export owing to the storage of Morakot-triggered landslide. This model will be a useful tool to diagnose the sediment dynamics in the watershed. [ABSTRACT FROM AUTHOR]

High mountain areas are very dynamic in terms of erosion and sedimentation. Highest geomorphic activity in central Nepal Himalaya is driven by monsoonal precipitation. Floods and landslides not only are relevant processes regarding landscape evolution but pose serious threats to local population and infrastructure. However, often infrastructure development increases geomorphic activity and natural risks.The Kali Gandaki valley is the deepest valley on earth, showing height differences of more than 5500m from valley bottom to the highest peaks of Dhaulagiri and Annapurna (>8000m). Currently, the Kali Gandaki road is extended to a two lane road, which leads to numerous undercut and instable slopes.Geomorphic activity and natural hazards were assessed between Tukuche and Dana based on the interpretation of high resolution Pleiades satellite images of March and October 2018 and field visits in April, September and November 2018. The analysis of the satellite images shows that during monsoonal precipitation at least one severe rainstorm occurred which led to a major flood and triggered numerous small and some bigger landslides. Especially north of Ghasa almost all tributary channels show significant erosional features and some also sedimentation. Also debris flows occurred. Not all events in the tributaries were connected to the Kali Gandaki river. North of Ghasa a steep section of the Dhampu rockslide was undercut by the major Kali Gandaki flood, triggering a 700m wide and 350m high slide which removed almost all regrown vegetation on this slope. It transported lots of sediments to the Kali Gandaki, increasing its erosive power. In addition, severe wind erosion has started at this site to blow large amounts of sediments towards Lete and Kalopani, impacting local population and the tourism industry there. Local residents stated that they did not have such dust problems before.The major flood caused severe erosion and sedimentation along the Kali Gandaki floodplain until south of Dana. In Bandarjung retrogressive erosion in the lower part of the steep tributary caused a road blockage for eleven days in August, which is supposed to be a good indication for the timing of the whole event. Further south, parts of the Talbagar landslide were reactivated, its crown getting close to the houses of PahiroThapla. Another landslide along the river bank destroyed three houses. Talus slopes along the road started to react upon the undercutting due to construction works. Current slope stabilization measures are not sufficient to stabilize these slopes. Without further stabilization measures the undercut talus slopes and landslides will provide a source for increased geomorphic activity and natural hazards for a long time. [ABSTRACT FROM AUTHOR]