scholarly journals Innovative Landslide Susceptibility Mapping Portrayed by CA-AQD and K-Means Clustering Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Mao Yimin ◽  
Li Yican ◽  
Deborah Simon Mwakapesa ◽  
Wang Genglong ◽  
Yaser Ahangari Nanehkaran ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Strong Dependence ◽  
Shaanxi Province ◽  
Landslide Susceptibility Mapping ◽  
Social And Economic Development ◽  
Characteristic Values ◽  
Quadratic Distance ◽  
Statistical Metrics

This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). The model was implemented in Baota District, Shaanxi province, China. The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. The model was then evaluated using statistical metrics and the performance was validated and compared to that of the traditional Chameleon algorithm and KPSO algorithm. The results show that the CA-AQD algorithm attained the best performance in assessing landslide susceptibility in the study area. Thus, this work adds to the literature by introducing the first empirical integration and application of the CA-AQD algorithm to the assessment of landslides in the study area, which then is a new insight to the field. Also, the method can be helpful for dealing with landslides for better social and economic development.

scholarly journals Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 868 ◽  
Author(s):  
Jie Liu ◽  
Zhao Duan
Keyword(s):  
Landslide Susceptibility ◽  
Weights Of Evidence ◽  
Susceptibility Mapping ◽  
Shaanxi Province ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Susceptibility Map ◽  
Statistical Index ◽  
Index Of Entropy ◽  
Testing Dataset

In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the most landslide-prone areas in Shaanxi Province, China, Shangnan County was selected as the study area. Firstly, a series of reports, remote sensing images and geological maps were collected, and field surveys were carried out to prepare a landslide inventory map. A total of 348 landslides were identified in study area, and they were reclassified as a training dataset (70% = 244 landslides) and testing dataset (30% = 104 landslides) by random selection. Thirteen conditioning factors were then employed. Corresponding thematic data layers and landslide susceptibility maps were generated based on ArcGIS software. Finally, the area under the curve (AUC) values were calculated for the training dataset and the testing dataset in order to validate and compare the performance of the three models. For the training dataset, the AUC plots showed that the WOE model had the highest accuracy rate of 76.05%, followed by the SI model (74.67%) and the IOE model (71.12%). In the case of the testing dataset, the prediction accuracy rates for the SI, IOE and WOE models were 73.75%, 63.89%, and 75.10%, respectively. It can be concluded that the WOE model had the best prediction capacity for landslide susceptibility mapping in Shangnan County. The landslide susceptibility map produced by the WOE model had a profound geological and engineering significance in terms of landslide hazard prevention and control in the study area and other similar areas.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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