scholarly journals Temporal Assessment on Variation of PM10 Concentration in Kota Kinabalu using Principal Component Analysis and Fourier Analysis

2019 ◽  
Vol 14 (3) ◽  
pp. 400-410 ◽  
Author(s):  
Muhammad Izzuddin Rumaling ◽  
Fuei Pien Chee ◽  
Jedol Dayou ◽  
Jackson Hian Wui Chang ◽  
Steven Soon Kai Kong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Wind Speed ◽  
Fourier Analysis ◽  
Data Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Particle Dispersion ◽  
Pm10 Concentration ◽  
Time Range ◽  
Temporal Data

PM10 (particulate matter with aerodynamic diameter below 10 microns) has always caught scientific attention due to its effect to human health. Predicting PM10 concentration is essential for early preventive measures, especially for cities such as Kota Kinabalu. Temporal data clustering may enhance accuracy of prediction model by group data in time range. However, the necessity of temporal data clustering has yet to be studied in Kota Kinabalu. OBJECTIVE. This research is conducted to compare significance of meteorological and pollutant factors for PM10 variation in clustered and unclustered data. METHODOLOGY. This study is focused in Kota Kinabalu, Sabah. The data for meteorological factors (Ws, Wd, Hum, Temp) and pollutant factors (CO2, NO2, O3, SO2, PM10) from 2003 to 2012 provided by Department of Environment are used for this research. Missing data are imputed using nearest neighbour method before it is clustered by monsoonal clustering. Unclustered and clustered datasets are analysed using principal component analysis (PCA) to check significance of factors contributing to PM10 concentration. FINDINGS. PCA results show that temporal clustering does not have noticeable effect on the variation of PM10 concentration. For all datasets, humidity and x-component wind speed have highest factor loading on PC1 and PC2 respectively. Further statistical analysis by 2-D regression shows that humidity (ρ = -0.60 ± 0.20) and temperature (ρ = 0.63 ± 0.11) have moderate to strong correlation towards PM10 concentration. This may be due to high humidity level and strong negative correlation between temperature and humidity (ρ = -0.91 ± 0.03). In contrast, both x- and y-component wind speed generally show weak correlation towards PM10, with ρ value of 0.09 ± 0.14 and 0.24 ± 0.18 respectively probably because of varying direction of particle dispersion. Fourier analysis further confirms this result by showing that human activity contributes major effect to variation of PM10 concentration.

scholarly journals Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

2016 ◽  
Vol 6 (1) ◽  
pp. 330 ◽  
Author(s):  
Se-Hoon Jung ◽  
Jong-Chan Kim ◽  
Chun-Bo Sim
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Data Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Model ◽  
Data Generation ◽  
Processing Scheme ◽  
Artificial Neural

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

scholarly journals PENGELOMPOKKAN DATA WAJAH MENGGUNAKAN METODE AGGLOMERATIVE CLUSTERING DENGAN ANALISIS KOMPONEN UTAMA

2011 ◽  
Vol 15 (1) ◽  
pp. 178
Author(s):  
Altien J Rindengan ◽  
Deiby Tineke Salaki
Keyword(s):  
Principal Component Analysis ◽  
Data Clustering ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Matlab Program

PENGELOMPOKKAN DATA WAJAH MENGGUNAKAN METODE AGGLOMERATIVE CLUSTERING DENGAN ANALISIS KOMPONEN UTAMA Altien J. Rindengan1) dan Deiby Tineke Salaki1) 1)Program Studi Matematika FMIPA Universitas Sam Ratulangi Manado 95115 ABSTRAK Pada penelitian ini dilakukan analisis pengelompokkan data wajah dengan analisis komponen utama untuk mengambil beberapa akar ciri yang cukup mewakili data tersebut dan pengelompokkannya menggunakan metode agglomerative clustering. Dengan menggunakan program Matlab, data wajah yang terdiri dari 6 orang dengan 10 image dapat dikelompokkan sesuai data aslinya.  Pengelompokkannya cukup menggunakan 3 akar ciri pada selang 68 %. Kata kunci: agglomerative clustering, analisis komponen utama, data wajah  FACE DATA CLUSTERING USING AGGLOMERATIVE CLUSTERING METHODS WITH PRINCIPAL COMPONENT ANALYSIS ABSTRACT In this research, face data is grouped using principal component analysis by getting some of its eigenvalues which are representative enough to describe the data and then by using agglomerative clustering the data is clustered.  By running the Matlab program, face data which is consist of 6 people with 10 images can be clustered to fit the original data.  The clustering is enough using 3 eigenvalues with 68 % of interval. Keywords: agglomerative clustering, principal component analysis, face data

Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

2016 ◽  
Vol 6 (1) ◽  
pp. 330 ◽  
Author(s):  
Se-Hoon Jung ◽  
Jong-Chan Kim ◽  
Chun-Bo Sim
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Data Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Learning Model ◽  
Data Generation ◽  
Processing Scheme ◽  
Artificial Neural

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

scholarly journals Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM

2020 ◽  
Vol 10 (13) ◽  
pp. 4416 ◽  
Author(s):  
Dawei Geng ◽  
Haifeng Zhang ◽  
Hongyu Wu
Keyword(s):  
Principal Component Analysis ◽  
Wind Speed ◽  
Principal Component ◽  
Component Analysis ◽  
Batch Size ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Lstm Network

An accurate prediction of wind speed is crucial for the economic and resilient operation of power systems with a high penetration level of wind power. Meteorological information such as temperature, humidity, air pressure, and wind level has a significant influence on wind speed, which makes it difficult to predict wind speed accurately. This paper proposes a wind speed prediction method through an effective combination of principal component analysis (PCA) and long short-term memory (LSTM) network. Firstly, PCA is employed to reduce the dimensions of the original multidimensional meteorological data which affect the wind speed. Further, differential evolution (DE) algorithm is presented to optimize the learning rate, number of hidden layer nodes, and batch size of the LSTM network. Finally, the reduced feature data from PCA and the wind speed data are merged together as an input to the LSTM network for wind speed prediction. In order to show the merits of the proposed method, several prevailing prediction methods, such as Gaussian process regression (GPR), support vector regression (SVR), recurrent neural network (RNN), and other forecasting techniques, are introduced for comparative purposes. Numerical results show that the proposed method performs best in prediction accuracy.

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