Water pollution index evaluation of lake based on principal component analysis
نویسندگان
چکیده
منابع مشابه
Development of Arab Water Sustainability Index Using Principal Component Analysis
The growing interest in the use of indices is closely connected to the increasing complexity of policy problems and the amount of available data. In the water sector, beyond their face value, indices can provide various types of information: description, communication, assessment, showing trends and predicting the future. Because the Arab world is the most water-scarce part of the world, most o...
متن کاملFace Recognition Based on Principal Component Analysis
The purpose of the proposed research work is to develop a computer system that can recognize a person by comparing the characteristics of face to those of known individuals. The main focus is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background will be constant. All the other methods of person’s identification and verification lik...
متن کاملSubpattern-Based Principal Component Analysis
We propose a subpattern-based principle component analysis (SpPCA). The traditional PCA operates directly on a whole pattern represented as a vector and acquires a set of projection vectors to extract global features from given training patterns. SpPCA operates instead directly on a set of partitioned subpatterns of the original pattern and acquires a set of projection sub-vectors for each part...
متن کاملFaults and fractures detection in 2D seismic data based on principal component analysis
Various approached have been introduced to extract as much as information form seismic image for any specific reservoir or geological study. Modeling of faults and fractures are among the most attracted objects for interpretation in geological study on seismic images that several strategies have been presented for this specific purpose. In this study, we have presented a modified approach of ap...
متن کاملCompression of Breast Cancer Images By Principal Component Analysis
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Earth and Environmental Science
سال: 2019
ISSN: 1755-1315
DOI: 10.1088/1755-1315/300/3/032010