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Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering

One approach to improve water resource management is by making use of streamflow forecasts. In this study, eigenvector pairs were clustered by employing fuzzy c-means (FCM) during the grouping stage as an enhancement to the singular spectrum analysis (SSA) technique for data pre-processing. The FCM-...

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Main Authors: Nasir, Najah, Samsudin, Ruhaidah, Shabri, Ani
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/92154/1/NajahNasir2020_PreprocessingStreamflowDatathroughSingularSpectrum.pdf
http://eprints.utm.my/id/eprint/92154/
http://dx.doi.org/10.1088/1757-899X/864/1/012085
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spelling my.utm.921542021-08-30T04:58:51Z http://eprints.utm.my/id/eprint/92154/ Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering Nasir, Najah Samsudin, Ruhaidah Shabri, Ani TJ Mechanical engineering and machinery One approach to improve water resource management is by making use of streamflow forecasts. In this study, eigenvector pairs were clustered by employing fuzzy c-means (FCM) during the grouping stage as an enhancement to the singular spectrum analysis (SSA) technique for data pre-processing. The FCM-SSA pre-processed streamflow data was then supplied to an auto-regressive integrated moving average (ARIMA) model for forecasting. The Department of Irrigation and Drainage Malaysia provided the monthly streamflow records of Sungai Muda (Jambatan Syed Omar) and Sungai Muda (Jeniang) for this research, wherein each was split into training (90%) and testing (10%) sets. The R software was the platform for building every FCM-SSA-ARIMA, SSA-ARIMA and ARIMA model, while the root mean squared errors and mean absolute errors were used to compare the performance between those models. The proposed FCM-SSA-ARIMA was discovered to be capable of surpassing the SSA-ARIMA and ARIMA models. 2020 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/92154/1/NajahNasir2020_PreprocessingStreamflowDatathroughSingularSpectrum.pdf Nasir, Najah and Samsudin, Ruhaidah and Shabri, Ani (2020) Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering. In: 2nd Joint Conference on Green Engineering Technology and Applied Computing 2020, IConGETech 2020 and International Conference on Applied Computing 2020, ICAC 2020, 4 - 5 February 2020, Bangkok, Thailand. http://dx.doi.org/10.1088/1757-899X/864/1/012085
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Nasir, Najah
Samsudin, Ruhaidah
Shabri, Ani
Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering
description One approach to improve water resource management is by making use of streamflow forecasts. In this study, eigenvector pairs were clustered by employing fuzzy c-means (FCM) during the grouping stage as an enhancement to the singular spectrum analysis (SSA) technique for data pre-processing. The FCM-SSA pre-processed streamflow data was then supplied to an auto-regressive integrated moving average (ARIMA) model for forecasting. The Department of Irrigation and Drainage Malaysia provided the monthly streamflow records of Sungai Muda (Jambatan Syed Omar) and Sungai Muda (Jeniang) for this research, wherein each was split into training (90%) and testing (10%) sets. The R software was the platform for building every FCM-SSA-ARIMA, SSA-ARIMA and ARIMA model, while the root mean squared errors and mean absolute errors were used to compare the performance between those models. The proposed FCM-SSA-ARIMA was discovered to be capable of surpassing the SSA-ARIMA and ARIMA models.
format Conference or Workshop Item
author Nasir, Najah
Samsudin, Ruhaidah
Shabri, Ani
author_facet Nasir, Najah
Samsudin, Ruhaidah
Shabri, Ani
author_sort Nasir, Najah
title Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering
title_short Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering
title_full Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering
title_fullStr Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering
title_full_unstemmed Pre-processing streamflow data through singular spectrum analysis with fuzzy C-means clustering
title_sort pre-processing streamflow data through singular spectrum analysis with fuzzy c-means clustering
publishDate 2020
url http://eprints.utm.my/id/eprint/92154/1/NajahNasir2020_PreprocessingStreamflowDatathroughSingularSpectrum.pdf
http://eprints.utm.my/id/eprint/92154/
http://dx.doi.org/10.1088/1757-899X/864/1/012085
_version_ 1709667391546327040
score 13.211869

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