A study on soft computing approach in weather forecasting

Weather forecasts based on temperature, wind speed and relative humidity are very important attributes in agriculture sector as well as many industries which largely depend on the weather condition. Therefore, having accurate weather forecasting information may allow farmers to make good decision on...

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Main Author:	Yen, Wee Khun
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11144/5/YenWeeKhunFSKSM2010.pdf
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id	my-utm-ep.11144
record_format	uketd_dc
spelling	my-utm-ep.111442017-09-26T05:01:38Z A study on soft computing approach in weather forecasting 2010-04 Yen, Wee Khun QA75 Electronic computers. Computer science Weather forecasts based on temperature, wind speed and relative humidity are very important attributes in agriculture sector as well as many industries which largely depend on the weather condition. Therefore, having accurate weather forecasting information may allow farmers to make good decision on managing their farm. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to processes humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. The weather forecasting model based on soft computing is easy to implement and produces desirable forecasting result by training on the given dataset. The technique of soft computing such as BPNN, RBFN, PSONN and ANFIS are used in this study to test the performance in order to investigate which technique for weather forecasting is most effective and least of error. 720 hours of Johor Bahru weather data are used in this study in order to test their result of prediction based on MSE and RMSE. Besides, the experiment regarding the effect of different input nodes which applies tapped delay line method and different hidden nodes are also used to investigate whether previous data affects the performance. The result shows that ANFIS with input temperature, humidity, wind speed, weather condition(t), and weather condition(t-1) with the previous data will give the lowest MSE and RMSE, 7.0853e-3 and 8.4174e-2 consequence than other soft computing approaches. 2010-04 Thesis http://eprints.utm.my/id/eprint/11144/ http://eprints.utm.my/id/eprint/11144/5/YenWeeKhunFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Yen, Wee Khun A study on soft computing approach in weather forecasting
description	Weather forecasts based on temperature, wind speed and relative humidity are very important attributes in agriculture sector as well as many industries which largely depend on the weather condition. Therefore, having accurate weather forecasting information may allow farmers to make good decision on managing their farm. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to processes humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. The weather forecasting model based on soft computing is easy to implement and produces desirable forecasting result by training on the given dataset. The technique of soft computing such as BPNN, RBFN, PSONN and ANFIS are used in this study to test the performance in order to investigate which technique for weather forecasting is most effective and least of error. 720 hours of Johor Bahru weather data are used in this study in order to test their result of prediction based on MSE and RMSE. Besides, the experiment regarding the effect of different input nodes which applies tapped delay line method and different hidden nodes are also used to investigate whether previous data affects the performance. The result shows that ANFIS with input temperature, humidity, wind speed, weather condition(t), and weather condition(t-1) with the previous data will give the lowest MSE and RMSE, 7.0853e-3 and 8.4174e-2 consequence than other soft computing approaches.
format	Thesis
qualification_level	Master's degree
author	Yen, Wee Khun
author_facet	Yen, Wee Khun
author_sort	Yen, Wee Khun
title	A study on soft computing approach in weather forecasting
title_short	A study on soft computing approach in weather forecasting
title_full	A study on soft computing approach in weather forecasting
title_fullStr	A study on soft computing approach in weather forecasting
title_full_unstemmed	A study on soft computing approach in weather forecasting
title_sort	study on soft computing approach in weather forecasting
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/11144/5/YenWeeKhunFSKSM2010.pdf
_version_	1747814818780282880

A study on soft computing approach in weather forecasting

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