1,720,968 research outputs found
Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models
Full text linkThe numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models
Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales
Full text linkThis study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%-10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.[GRAPHICS]
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Optimization method of conditioning factors selection and combination for landslide susceptibility prediction
Full text linkLandslide susceptibility prediction (LSP) is significantly affected by the uncertainty issue of landslide related conditioning factor selection. However, most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue. Targeted, this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP, and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors. An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors. Five commonly used factor selection methods, namely, the correlation analysis (CA), linear regression (LR), principal component analysis (PCA), rough set (RS) and artificial neural network (ANN), are applied to select the optimal factor combinations from the original 29 conditioning factors. The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models, such as CA-multilayer perceptron, CA-random forest. Additionally, multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of “accurate data, rich types, clear significance, feasible operation and avoiding duplication” are constructed for comparisons. Finally, the LSP uncertainties are evaluated by the accuracy, susceptibility index distribution, etc. Results show that: (1) multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models; (2) Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods. Conclusively, the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes. In contrast, a satisfied combination of conditioning factors can be constructed according to the proposed principle
Effects of different division methods of landslide susceptibility levels on regional landslide susceptibility mapping
No full textReasonable division methods of landslide susceptibility indexes (LSIs) are crucial for producing landslide susceptibility levels (LSLs), including very low, low, moderate, high, and very high levels. However, few studies have systematically compared division methods such as natural break, equal interval, quantile, geometric interval, and K-means. Moreover, these methods start from LSIs but ignore the nonlinear correlation between known landslides and LSIs. To address this, the natural break-frequency ratio (FR) method is proposed, combining the natural break method for LSLs division with the FR method. First, the five conventional methods divide LSIs predicted by three machine learning models in An’yuan County, China. Then, the natural break-FR method is proposed to divide the same LSIs and compared with these methods. The natural break-FR, equal interval and K-means method yielded the largest sum of landslide ratio in very high and high susceptibility level, showing these methods can use high and very high susceptibility levels to predict as many landslides as possible. Finally, statistical perspectives of known landslide identification, division area proportion, and landslide ratio are applied to discuss how to select a suitable division method. Results show different division methods have comparative effects on final LSLs. The landslide ratios of equal interval, K-means, and natural break methods at high and very high susceptibility levels are greater than the former methods. The natural break-FR method performs best with MLP and SVM, but in the more precise RF model, the equal interval method outperforms it, followed by the natural break-FR method
A hierarchical graph-based hybrid neural networks with a self-screening strategy for landslide susceptibility prediction in the spatial–frequency domain
No full textLandslide susceptibility prediction (LSP) is a complex task with unresolved uncertainties, such as errors in sample classification and intricate relationships among environmental factors and spatial grid units. Additionally, the absence of interpretable black box models restricts the credibility and effectiveness of prediction models. To tackle these problems, an innovative interpretable deep learning model based on self-filtering graph convolutional networks and long short-term memory (SGCN-LSTM) is proposed. In the SGCN-LSTM, a self-screening strategy is employed to remove landslide/non-landslide samples with substantial errors that fall outside a defined threshold interval. Furthermore, SGCN-LSTM extracts nonlinear connections between environmental factors and long-range dependencies among grid units through spatial nodes and information gates. The Anyuan County in south China, with 2,655,972 grid units, 16,594 labeled, served as the study area. The LSP models used numeric inputs from the Frequency Ratios of 10 environmental factors in these spatial grid units. Results show that the accuracy and area AUC of the SGCN-LSTM achieve 92.38% and 0.9782, which are higher than those of one deep learning model cascade-parallel long short-term memory and conditional random fields (by 5.88% and 0.0305), and four machine learning models (by 12.44-20.34% and 0.0532–0.1909). This article delves into SGCN-LSTM ‘s evaluation results using the SHAP method, providing insights into the landslide development patterns and spatial heterogeneity of associated environmental factors in Anyuan County, with a global interpretability perspective. In conclusion, the SGCN-LSTM automatically screens erroneous samples, effectively extracts nonlinear features and spatial relationships from various environmental factors and delivers superior prediction accuracy and robustness for LSP
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Uncertainties in landslide susceptibility prediction: Influence rule of different levels of errors in landslide spatial position
Full text linkThe accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modeling. To overcome this drawback, this study explores the influence of positional errors of landslide spatial position on LSP uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error. This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example. The 30–110 m error-based multilayer perceptron (MLP) and random forest (RF) models for LSP are established by randomly offsetting the original landslide by 30, 50, 70, 90 and 110 m. The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics. Finally, a semi-supervised model is proposed to relieve the LSP uncertainties. Results show that: (1) The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors, and are lower than those of original data-based models; (2) 70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices, thus original landslides with certain position errors are acceptable for LSP; (3) Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies
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