Staff View: Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning

Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning

Given the growing demand for comprehensive meteorological data for further studies and project management, various machine learning methods for the restoration of missing data have been proposed in recent years, all of which are progressively more advanced than the conventional methods to fill the m...

Full description

Saved in:

Bibliographic Details
Main Author:	Lee, Shuan Chung
Format:	Final Year Project / Dissertation / Thesis
Published:	2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utar.edu.my/4243/1/1800121_FYP_Report_%2D_LEE_SHUAN_CHUNG.pdf http://eprints.utar.edu.my/4243/
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utar-eprints.4243
record_format	eprints
spelling	my-utar-eprints.42432021-12-10T14:08:07Z Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning Lee, Shuan Chung TA Engineering (General). Civil engineering (General) Given the growing demand for comprehensive meteorological data for further studies and project management, various machine learning methods for the restoration of missing data have been proposed in recent years, all of which are progressively more advanced than the conventional methods to fill the missing value in a data. Six different models involving feedforward neural networks such as multilayer perceptron (MLP) and radial basis function (RBF), and swarm intelligence optimization such as particle swarm optimization (PSO) and artificial bee colony (ABC), have been proposed to restore missing meteorological data. Proposed models are applied to complete a fifteen-year time series data of minimum temperature, maximum temperature, mean temperature, relative humidity, average wind speed, and evaporation data at twelve different weather stations in Malaysia. The models are validated by the statistical performance measures of root mean square error, mean absolute error and mean absolute percentage error. From the results, the MLP model has generated lower errors as compared to the RBF model. When comparing the single model with the hybrid model, the hybrid models, namely PSO-MLP, ABC-MLP, PSO-RBF, and ABC-RBF, have outperformed the single model and replaced the single model as the standout model for most meteorological parameters and weather stations. The performance of the models has also varied among the different locations. Results for the stations at the northern region of West Malaysia have generated higher errors, while the results at East Malaysia have generated lower errors. It can be concluded that the proposed models have shown suitability for imputing missing meteorological data and are thus recommended for other uses in other sectors where the missing values are of concern. 2021 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4243/1/1800121_FYP_Report_%2D_LEE_SHUAN_CHUNG.pdf Lee, Shuan Chung (2021) Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning. Final Year Project, UTAR. http://eprints.utar.edu.my/4243/
institution	Universiti Tunku Abdul Rahman
building	UTAR Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tunku Abdul Rahman
content_source	UTAR Institutional Repository
url_provider	http://eprints.utar.edu.my
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Lee, Shuan Chung Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning
description	Given the growing demand for comprehensive meteorological data for further studies and project management, various machine learning methods for the restoration of missing data have been proposed in recent years, all of which are progressively more advanced than the conventional methods to fill the missing value in a data. Six different models involving feedforward neural networks such as multilayer perceptron (MLP) and radial basis function (RBF), and swarm intelligence optimization such as particle swarm optimization (PSO) and artificial bee colony (ABC), have been proposed to restore missing meteorological data. Proposed models are applied to complete a fifteen-year time series data of minimum temperature, maximum temperature, mean temperature, relative humidity, average wind speed, and evaporation data at twelve different weather stations in Malaysia. The models are validated by the statistical performance measures of root mean square error, mean absolute error and mean absolute percentage error. From the results, the MLP model has generated lower errors as compared to the RBF model. When comparing the single model with the hybrid model, the hybrid models, namely PSO-MLP, ABC-MLP, PSO-RBF, and ABC-RBF, have outperformed the single model and replaced the single model as the standout model for most meteorological parameters and weather stations. The performance of the models has also varied among the different locations. Results for the stations at the northern region of West Malaysia have generated higher errors, while the results at East Malaysia have generated lower errors. It can be concluded that the proposed models have shown suitability for imputing missing meteorological data and are thus recommended for other uses in other sectors where the missing values are of concern.
format	Final Year Project / Dissertation / Thesis
author	Lee, Shuan Chung
author_facet	Lee, Shuan Chung
author_sort	Lee, Shuan Chung
title	Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning
title_short	Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning
title_full	Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning
title_fullStr	Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning
title_full_unstemmed	Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning
title_sort	restoration of missing meteorological data for long term monitoring using machine learning
publishDate	2021
url	http://eprints.utar.edu.my/4243/1/1800121_FYP_Report_%2D_LEE_SHUAN_CHUNG.pdf http://eprints.utar.edu.my/4243/
_version_	1724078893863796736
score	13.223943

Restoration Of Missing Meteorological Data For Long Term Monitoring Using Machine Learning

Similar Items