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Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems. [ABSTRACT FROM AUTHOR]

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]

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]

Myrtos Beach (Cephalonia Island, Ionian Sea, Greece) represents a pocket beach with strong touristic, economic and natural interest. In this research, the morphodynamic behavior of the coastal area (e.g., hydrodynamic and sedimentary state, morphology, orientation, etc.), the current wave conditions (extreme and dominant waves, wave exposure), and also external factors, such as human impact and the geotechnical condition of the wider area, are examined. Short- and medium-to-long-term analysis took place, such as mapping, sediment analysis, wave/wind analysis, numerical modeling, and satellite monitoring, in order to identify the dynamic forcing parameters related to geomorphology, sedimentology, and hydrology that prevail in the area. Additionally, the intense tectonics, the karstified limestones, and the steep slopes of the cliffs in combination with the frequent seismic events on the island set up a geotechnically unstable area, which often cause landslides on the beach of Myrtos; these supply the beach with a large amount of aggregates, constituting the main sediment supply. Wave exposure forcing conditions, longshore–rip current direction, and other hydrodynamic processes are stable with high values in the area, causing notable sediment transport within the bay boundaries. As a result, at Myrtos Bay there is a dynamic balance of the natural system, which is directly affected by human interventions. Taking also into consideration that Myrtos is one of the most famous beaches in Greece and one of the main attractions of Cephalonia Island with thousands of visitors every year, beach management must be focused on preserving the natural system of the coastal area. [ABSTRACT FROM AUTHOR]

The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area. [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]

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]

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance. [ABSTRACT FROM AUTHOR]

Sediment yield is a complex phenomenon of weathering, land sliding, and glacial and fluvial erosion. It is highly dependent on the catchment area, topography, slope of the catchment terrain, rainfall, temperature, and soil characteristics. This study was designed to evaluate the key hydraulic parameters of sediment transport for Kali Gandaki River at Setibeni, Syangja, located about 5 km upstream from a hydropower dam. Key parameters, including the bed shear stress (τb), specific stream power (ω), and flow velocity (v) associated with the maximum boulder size transport, were determined throughout the years, 2003 to 2011, by using a derived lower boundary equation. Clockwise hysteresis loops of the average hysteresis index of +1.59 were developed and an average of 40.904 ± 12.453 Megatons (Mt) suspended sediment have been transported annually from the higher Himalayas to the hydropower reservoir. Artificial neural networks (ANNs) were used to predict the daily suspended sediment rate and annual sediment load as 35.190 ± 7.018 Mt, which was satisfactory compared to the multiple linear regression, nonlinear multiple regression, general power model, and log transform models, including the sediment rating curve. Performance indicators were used to compare these models and satisfactory fittings were observed in ANNs. The root mean square error (RMSE) of 1982 kg s−1, percent bias (PBIAS) of +14.26, RMSE-observations standard deviation ratio (RSR) of 0.55, coefficient of determination (R2) of 0.71, and Nash–Sutcliffe efficiency (NSE) of +0.70 revealed that the ANNs' model performed satisfactorily among all the proposed models. [ABSTRACT FROM AUTHOR]

Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial damming and sedimentation is disputed in the literature due to the lack of a historical time series. In this paper, we present an analysis of the lake's evolution from 1926 to 2018 and model the 50-year trajectory of shrinkage. The area of Phewa Lake expanded from 2.44 ± 1.02 km2 in 1926 to a maximum of 4.61 ± 0.07 km2 in 1961. However, the lake area change was poorly constrained prior to a 1957–1958 map. The contemporary lake area was 4.02 ± 0.07 km2 in April 2018, and expands seasonally by ~0.18 km2 due to the summer monsoon. We found no evidence to support a lake area of 10 km2 in 1956–1957, despite frequent reporting of this value in the literature. Based on the rate of areal decline and sediment influx, we estimate the lake will lose 80% of its storage capacity in the next 110–347 years, which will affect recreational use, agricultural irrigation, fishing, and a one-megawatt hydroelectric power facility. Mitigation of lake shrinkage will require addressing landslide activity and sediment transport in the watershed, as well as urban expansion along the shores. [ABSTRACT FROM AUTHOR]

Connectivity of landslide sediment to and within fluvial systems is a key factor affecting the extent of mobilization of hillslope material. In particular, the formation of landslide dams and the transformation into landslide-induced debris flows represent "end members" of landslide sediment mobility. To quantify sediment connectivity, we developed a two-segment flume representing tributary inflow and the main channel. Mobility of sediment was examined by combinations of various topographic factors, such as tributary inflow angle (0 to 90° in 30° increments) and main channel gradient (10° and 15°), as well as water content of sediment (0 to 100% in 20% increments). We also examined differences of mobility among sediments derived from various lithologies (sand and shale, pyroclastic sediment, weathered granite, and weathered sedimentary rock). Mobility of sediment differed, depending on the water content of sediment, particularly less than saturation or greater than saturation. When all types of unsaturated landslide sediments entered the channel at inflow angles of 60° and 90°, substantial deposition occurred, suggesting the formation of landslide dams. At low inflow angles (0° and 30°) in a steep channel (15°), >50% of landslide sediment was transported downstream, indicating the occurrence of a debris flow. The amount of sediment deposited at the junction angle was greater for pyroclastic sediment followed by weathered granite, weathered sedimentary rock, and finally, sand and shale. Our connectivity index suggests that a threshold exists between landslide dam formation and debris flow occurrence associated with topographic conditions, water content, and types of sediment. [ABSTRACT FROM AUTHOR]

The Wenchuan Earthquake took place in the upper reach catchment of the Min River. It resulted in large amounts of loose materials gathering in the river channel, leading to changes in the sediment transport system in this area. The Zipingpu Reservoir is the last and the largest reservoir located in the upper reach of the Min River. It is near the epicenter and receives sediment from upstream. This paper puts forward a study on the reservoir sedimentation and storage capacity of the Zipingpu Reservoir, employing a multi-beam echo-sounder system in December 2012. Then, the data were merged with digital line graphics and shuttle radar topography mission data in ArcGIS to build a digital elevation model and triangulate the irregular network of Zipingpu Reservoir. Via the analysis of the bathymetric data, the results show the following: (1) The main channels of the reservoir gradually aggrade to a flat bottom from the deep-cutting valley. Sedimentation forms a reach with a W-shaped longitudinal thalweg profile and an almost zero slope reach in the upstream section of the reservoir due to the natural barrier induced by a landslide; (2) The loss ratios of the wetted cross-section surface are higher than 10% in the upstream section of the reservoir and higher than 40% in the natural barrier area; (3) Comparing the surveyed area storage capacity of December 2012 with March 2008, the Zipingpu Reservoir has lost 15.28% of its capacity at the dead storage water level and 10.49% of its capacity at the flood limit water level. [ABSTRACT FROM AUTHOR]