Search

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]

Collapsing gully is one of the most serious ecological and environmental damage in southern China. The dynamic change of wall collapse is also difficult to monitor in the high-risk and complex terrain. Obviously, a more concise and accurate method is necessary to monitor the dynamic collapse, and further to clarify the collapsing gully erosion. Unmanned Aerial Vehicle (UAV) nap of the object photograph is brand new photogrammetry for the needs of fine measurement. Point cloud data was generated to extract different parameters of collapse terrain. The topographic features of the avalanche wall were extracted, and then the obtained data was used to analyze the spatial characteristics and soil migration changes under the photogrammetry. Collapsing gully research area was located in the AnXi County, Fujian Province of South China. The specific procedure was as follows. 1) The control points were arranged in the study area, where the coordinates of each control point were measured using Trimble R4 GNSS GPS, Trimble R2 GPS, and (1+1) RTK network mode. 2) The image data was acquired by the nap of photogrammetry technology. PIX4DMapper was utilized to evaluate the positioning accuracy of control point data obtained by RTK. 3) The Digital Surface Model (DSM) data in the study area was processed by ArcMap, whereas, the digital surface model of differences (DOD) values was obtained for measured accuracy. The DOD was equivalent to the change of soil erosion and deposition volume, accurately reflecting the volume change of soil dynamic migration. The elevation change was also measured in the research area during the monitoring period. Through CloudCompare, Scale-invariant feature transform-Clustering Views for Multi-View Stereo (SFM-MVS) technology was used to analyze the reproducibility of point cloud data. A Canon 5D Mark III SLR camera was applied to the surround fixed focus-range image to collect data, and simultaneously to measure the target wall collapse as the reference data. Three groups of control plots were arranged according to the lowest level (LODmin) in the study area. The error sources and feasibility close to photogrammetry were analyzed from the accuracy of positioning and measurement, as well as the point cloud reproducibility. 4) The distribution characteristics of landslide erosion deposition were evaluated during the monitoring period, combined with the rainfall data collected by an RG3-M rainfall recorder. The results showed that: 1) The average resolution of the image close to the photogrammetry technology was 4.1 mm in terms of positioning. The average projection error was controlled at about 0.19 mm in terms of control point calibration. 2) The total mean absolute error of DOD in measurement was 0.006 m³, 45.45% lower than that of traditional oblique photogrammetry. 3) The overall improvement was 162.5% in elevation accuracy. 4) In reproducibility, the average value of elevation error was only 0.36 mm for the repeated point cloud data, indicating high stability. 5) The dynamic change of erosion settlement in the collapsed wall was monitored in the study area during the study period. The total rainfall was 564.6 mm in the study area, while the soil loss of the collapsed wall was 4.758 m³. The UAV Nap of the object photograph can be expected to serve as efficient and accurate monitoring for the erosion sediment transport in south collapsing gully areas of China. [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]

• Groundwater spatial modeling using spring data set. • Presenting new hybrid models of ANFIS-TLBO and ANFIS-BBO for groundwater potential mapping and its zoning. • Predictive capability of spring affecting factors using correlation attribute evaluation method. • Correlation between springs and affecting factors using the PCF algorithm. • ANFIS-TLBO shows the better results in term of AUC value. Groundwater is one of the most beneficial natural resources worldwide. The main aim of the present study is the mapping of groundwater potential of the Zhangjiamao area in China using two novel hybrid data mining techniques that involve the adaptive neuro-fuzzy inference system (ANFIS) ensembled with teaching-learning-based optimization (TLBO) and biogeography-based optimization (BBO). First, 93 spring locations were identified in the study area, which were randomly divided in the ratio of 70:30 to be used for building models and validation, respectively. A total of 16 spring affecting factors viz., slope aspect, altitude, slope angle, plan curvature, profile curvature, curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to rivers, rainfall, lithology, soil, NDVI, and land use/land cover, were used as input variables. The probability certainty factor (PCF) method was applied for the correlation analysis between spring occurrences and the affecting factors. Subsequently, the ANFIS-TLBO and ANFIS-BBO models were generated to produce groundwater spring potential maps (GSPMs). Finally, the GSPMs were evaluated by using the area under the receiver operating characteristic (AUROC). The results showed that the AUROC values for the ANFIS-TLBO model based on the training and validation datasets were 0.866 and 0.905, respectively. In contrast, the AUROC values for the ANFIS-BBO model based on the training and validation datasets were 0.861 and 0.887, respectively. The results of ANFIS-TLBO showed that 25.66% of the study area could be classified into the high and very high groundwater spring potential classes as compared to the ANFIS-BBO model (30.52%). The results of the present study can be useful for groundwater management. [ABSTRACT FROM AUTHOR]

Abstract The main objective of the present study was to produce a novel ensemble data mining technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) for spatial modeling of landslide susceptibility. Step-wise Assessment Ratio Analysis (SWARA) was utilized for the evaluation of the relation between landslides and landslide-related factors providing ANFIS with the necessary weighting values. The developed methods were applied in Langao County, Shaanxi Province, China. Eighteen factors were selected based on the experience gained from studying landslide phenomena, the local geo-environmental conditions as well as the availability of data, namely; elevation, slope aspect, slope angle, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, land use, normalized difference vegetation index, rainfall, lithology, distance to faults, fault density, distance to roads, road density, distance to rivers and river density. A total of 288 landslides were identified after analyzing previous technical surveys, airborne imagery and conducting field surveys. Also, 288 non-landslide areas were identified with the usage of Google Earth imagery and the analysis of a digital elevation model. The two datasets were merged and later divided into two subsets, training and testing, based on a random selection scheme. The produced landslide susceptibility maps were evaluated by the receiving operating characteristic and the area under the success and predictive rate curves (AUC). The results showed that AUC based on the training and testing dataset was similar and equal to 0.89. However, the processing time during the training and implementation phase was considerable different. SWARA-ANFIS-PSO appeared six times faster in respect to the processing time achieved by SWARA-ANFIS-SFLA. The proposed novel approach, which combines expert knowledge, neuro-fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility. Graphical abstract Unlabelled Image Highlights • Novel ensemble data mining technique for landslide susceptibility assessments • Step-wise Assessment Ratio Analysis for weighting landslide related variables • Adaptive neuro-fuzzy inference system (ANFIS) optimized by evolutionary algorithms • Integrate Shuffled Frog Leaping Algorithm, Particle Swarm Optimization and ANFIS [ABSTRACT FROM AUTHOR]

• A landslide sediment supply capacity assessment model was developed for applications in debris flow basins. • Spatial and temporal variability of landslide sediment supply capacity is significant. • Decay of landslide sediment supply capacity to the pre-earthquake level is expected within 17 years. • The evolution of landslide activity has a positive effect on the landslide sediment transport capacity. After the Wenchuan earthquake, a large number of landslides in debris flow basins continued to provide loose debris for channels and further affected the occurrence of debris flows. Evaluating the landslide sediment supply capacity in debris flow basins requires understanding the landslide activity and the coupled relationship between the landslide and the channel. In this study, we proposed a model based on the landslide area density index (P Als) and the landslide connectivity (IC l) to evaluate the capacity of the landslide sediment supply for channels in 46 sub-catchments in the study area. The capacity of the landslide sediment supply for channels generally decreased from 2008 to 2018, but due to the influence of heavy rainfall, the actual capacity of the landslide sediment supply for channels in the study area may gradually diminish in an unstable way, and it may arrive at the pre-earthquake level by 2025 without the influence of extreme rainfall. However, higher debris flow activity may last longer than 2025 because sufficient gully materials are still accumulated in the channel. The sediment transport capacity of landslides was divided into five levels: extremely, high, moderate, slight and low. The capacity of the landslide sediment supply for channels changed from extreme levels and high levels to moderate levels and slight levels after the first 5 years of the Wenchuan earthquake and was then dominated by low levels. Simultaneously, the regions with higher landslide sediment supply capacities gradually changed from along the Minjiang River regions to the upper and middle reaches of the debris flow basin from 2008 to 2018. Hongchun gully, which is in the study area, was chosen as an example to compare the assessment results and field investigation. The long-term evolution of landslides is an important reason for the decrease in the sediment supply capacity of landslides. [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]