Classification of aquifer vulnerability using K-means cluster analysis

Javadi, Saman; Hashemy, S. M.; Mohammadi, Kourosh; Howard, Ken; Neshat, Aminreza

doi:10.1016/j.jhydrol.2017.03.060

Cited by 115 publications

(44 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cluster analysis is the general logic, formulated as a procedure, by which we objectively group the entities together on the basis of their similarities and differences [47][48][49][50].…”

Section: Cluster Analysis Of Spatial Patterns Of Overall Natural Envimentioning

confidence: 99%

Modeling and Simulation of the Future Impacts of Urban Land Use Change on the Natural Environment by SLEUTH and Cluster Analysis

Kuo

Tsou

2017

Sustainability

View full text Add to dashboard Cite

Abstract:Changing land use and urban expansion are key drivers of global environmental change, which are inevitable consequences of economic and social development for many cities. Most land use changes have a negative impact on the natural environment, especially due to their effects on surface temperature, runoff and habitat diversity. Due to the limitation of local government funding and expenditure, it is a challenge for developing countries to create strategies for urban sustainability. This study provided a systematic assessment method for simulating and analyzing the future impacts and spatial patterns of urban growth via cellular automata and cluster analysis. We used Tainan as a study area and compared the impact of future urban spatial development during two periods : 1993-2008 and 2008-2030. The results indicate that the impact of this development on the natural environment can be divided into six clusters. With an increased distance from the city center, there were increased changes in surface temperature and a decreased amount of runoff. These results indicate the occurrence of urban expansion, with habitat diversity being greater in areas governed by policies or ordinances.

show abstract

“…Cluster analysis is the general logic, formulated as a procedure, by which we objectively group the entities together on the basis of their similarities and differences [47][48][49][50].…”

Section: Cluster Analysis Of Spatial Patterns Of Overall Natural Envimentioning

confidence: 99%

Modeling and Simulation of the Future Impacts of Urban Land Use Change on the Natural Environment by SLEUTH and Cluster Analysis

Kuo

Tsou

2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…The different subbands after wavelet decomposition are clustered to get the segmented tumor image [16]. Figure 8 shows the segmentation process.…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Yuan

Liu

2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Purpose. The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC). Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic. Methods. Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and K-means algorithm is proposed. Secondly, TA was performed and the Gabor wavelet analysis is used to extract the texture feature of an MRI tumor. Then, according to the distance relationship between the features, key features are sorted and feature subsets are selected. Finally, the feature subset is classified by using a support vector machine and adjusted parameters to achieve the best classification effect. Results. By selecting key features for classification prediction, the classification accuracy of the classification model can reach 81.33%. 3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%. Conclusion. The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors. This method can provide much valuable information for doctors’ clinical diagnosis. With further development, the model demonstrates high potential for practical clinical use.

show abstract

“…Then, a classification can use the k-means algorithm as a process of grouping a similar object into different groups. K-means algorithm uses the process repeatedly to get the cluster [6]. It takes the desired number of initial clusters as input and produces the final centroid point as output.…”

Section: Fig 2 the Proportion Of Faces [5]mentioning

confidence: 99%

Classification of Student Faces in Assignment Presentation Video using Bezier Curve and K-Means Clustering Algorithm

Sfenrianto*,

Nainggolan,

Rohman

2019

IJITEE

View full text Add to dashboard Cite

Grouping facial images can be used for various purposes with various algorithms. The purpose of this study was to determine the classification of students' facial images in the assignment presentation video using the Bezier Curve algorithm and K-Means Clustering. The results showed that the initial classification was still in a varied and uneven position. Then the final classification has a significantly different distribution for each cluster.

show abstract

Classification of aquifer vulnerability using K-means cluster analysis

Cited by 115 publications

References 25 publications

Modeling and Simulation of the Future Impacts of Urban Land Use Change on the Natural Environment by SLEUTH and Cluster Analysis

Modeling and Simulation of the Future Impacts of Urban Land Use Change on the Natural Environment by SLEUTH and Cluster Analysis

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Classification of Student Faces in Assignment Presentation Video using Bezier Curve and K-Means Clustering Algorithm

Contact Info

Product

Resources

About