Delineation of homogeneous regions for streamflow via fuzzy c-means in the Amazon

2018 ◽  
Vol 13 (1) ◽  
pp. 210-218 ◽  
Author(s):  
Francisco Carlos Lira Pessoa ◽  
Claudio José Cavalcante Blanco ◽  
Evanice Pinheiro Gomes
Keyword(s):  
Cluster Analysis ◽  
Optimal Number ◽  
Amazon Region ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Important Stage ◽  
Streamflow Data ◽  
Homogeneous Regions ◽  
Climatic Characteristics ◽  
Optimal Number Of Clusters

Abstract Lack of streamflow data is one of the main limitations in hydrologic studies. One method of solving this problem is by streamflow regionalization. The identification of hydrologically homogeneous regions is the main and most important stage of regionalization. In this study homogeneous flow regions are identified by fuzzy c-means (FCM) cluster analysis based on morpho-climatic characteristics from streamflow at 208 stream gauges in the Amazon region. The optimal number of clusters in the dataset was identified by applying the PBM validation index, maximized for ten clusters, with a fuzzing parameter of 1.6. The application dataset is best divided into 10 groups. These were well defined and demonstrated the Amazon's hydrologic similarity.

scholarly journals A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

2017 ◽  
Vol 65 (4) ◽  
pp. 359-365 ◽  
Author(s):  
Javier Senent-Aparicio ◽  
Jesús Soto ◽  
Julio Pérez-Sánchez ◽  
Jorge Garrido
Keyword(s):  
Frequency Analysis ◽  
Fuzzy Clustering ◽  
Optimal Number ◽  
Regional Frequency Analysis ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Homogeneous Regions ◽  
Optimal Number Of Clusters

AbstractOne of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.

Research on Fuzzy Clustering Validity

2010 ◽  
Vol 40-41 ◽  
pp. 174-182
Author(s):  
Wei Jin Chen ◽  
Huai Lin Dong ◽  
Qing Feng Wu ◽  
Ling Lin
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Optimal Number ◽  
Number Of Clusters ◽  
Fuzzy Partition ◽  
Geometry Structure ◽  
Clustering Validity ◽  
Optimal Number Of Clusters

The evaluation of clustering validity is important for clustering analysis, and is one of the hottest spots of cluster analysis. The quality of the evaluation of clustering is that optimal number of clusters is reasonable. For fuzzy clustering, the paper surveys the widely known fuzzy clustering validity evaluation based on the methods of fuzzy partition, geometry structure and statistics.

scholarly journals Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters

2014 ◽  
Vol 4 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Min Chen ◽  
Simone A. Ludwig
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Particle Swarm ◽  
Optimal Number ◽  
Swarm Optimization ◽  
Number Of Clusters ◽  
Sammon Mapping ◽  
Clustering Approach ◽  
Optimal Number Of Clusters

Abstract Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used

Improved Fuzzy C-Means Based on the Optimal Number of Clusters

2013 ◽  
Vol 392 ◽  
pp. 803-807 ◽  
Author(s):  
Xue Bo Feng ◽  
Fang Yao ◽  
Zhi Gang Li ◽  
Xiao Jing Yang
Keyword(s):  
Convergence Rate ◽  
Clustering Algorithm ◽  
Optimal Number ◽  
Data Set ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Initial Cluster ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Optimal Number Of Clusters

According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The improved FCM has good clustering effect, enhances the optimized capability, and improves the efficiency and effectiveness of the clustering. It has better tightness in the class, scatter among classes and cluster stability and faster convergence rate than the traditional FCM clustering method.

scholarly journals Clustering Methods for Power Quality Measurements in Virtual Power Plant

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5902
Author(s):  
Fachrizal Aksan ◽  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Jacek Rezmer ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Power Plant ◽  
Power Quality ◽  
Optimal Number ◽  
Operating Conditions ◽  
Virtual Power ◽  
Number Of Clusters ◽  
Virtual Power Plant ◽  
Agglomerative Algorithm ◽  
Optimal Number Of Clusters

In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.

Selection of global climate models for India using cluster analysis

2016 ◽  
Vol 7 (4) ◽  
pp. 764-774 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
Cluster Analysis ◽  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Optimal Number ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Number Of Clusters ◽  
Optimal Number Of Clusters

Global climate models (GCMs) are gaining importance due to their capability to ascertain climate variables that will be useful to develop long, medium and short term water resources planning strategies. The applicability of K-Means cluster analysis is explored for grouping 36 GCMs from Coupled Model Intercomparison Project 5 for maximum temperature (MAXT), minimum temperature (MINT) and a combination of maximum and minimum temperature (COMBT) over India. Cluster validation methods, namely the Davies–Bouldin Index (DBI) and F-statistic, are used to obtain an optimal number of clusters of GCMs for India. The indicator chosen for evaluation of GCMs is the probability density function based skill score. It is noticed that the optimal number of clusters for MAXT, MINT and COMBT scenarios are 3, 2 and 2, respectively. Accordingly, suitable ensembles of GCMs are suggested for India for MAXT, MINT and COMBT individually. The suggested methodology can be extended to any number of GCMs and indicators, with minor modifications.

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