表紙画像

Artificial neural network and support vector machine in flood forecasting: A review

Flood is a natural phenomenon that can cause havocs and deaths.Although flood is sometimes unavoidable, early flood forecasting can be helpful for people to take precaution.In the past decades, researchers have been working on flood forecasting models using artificial intelligence (AI).AI models suc...

詳細記述

保存先:
書誌詳細
主要な著者: Suliman, Azizah, Nazri, Nursyazana, Othman, Marini, Abdul Malek, Marlinda, Ku-Mahamud, Ku Ruhana
フォーマット: Conference or Workshop Item
言語:English
出版事項: 2013
主題:
QA76 Computer software
オンライン･アクセス:http://repo.uum.edu.my/12033/1/PID30.pdf
http://repo.uum.edu.my/12033/
http://www.icoci.cms.net.my/proceedings/2013/TOC.html
タグ: タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
id my.uum.repo.12033
record_format eprints
spelling my.uum.repo.120332014-08-25T07:04:33Z http://repo.uum.edu.my/12033/ Artificial neural network and support vector machine in flood forecasting: A review Suliman, Azizah Nazri, Nursyazana Othman, Marini Abdul Malek, Marlinda Ku-Mahamud, Ku Ruhana QA76 Computer software Flood is a natural phenomenon that can cause havocs and deaths.Although flood is sometimes unavoidable, early flood forecasting can be helpful for people to take precaution.In the past decades, researchers have been working on flood forecasting models using artificial intelligence (AI).AI models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been developed and implemented in different locations to help in weather forecasting over the past years. This paper reviews both methods and compares their experimental results. 2013-08-28 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/12033/1/PID30.pdf Suliman, Azizah and Nazri, Nursyazana and Othman, Marini and Abdul Malek, Marlinda and Ku-Mahamud, Ku Ruhana (2013) Artificial neural network and support vector machine in flood forecasting: A review. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia. http://www.icoci.cms.net.my/proceedings/2013/TOC.html
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Suliman, Azizah
Nazri, Nursyazana
Othman, Marini
Abdul Malek, Marlinda
Ku-Mahamud, Ku Ruhana
Artificial neural network and support vector machine in flood forecasting: A review
description Flood is a natural phenomenon that can cause havocs and deaths.Although flood is sometimes unavoidable, early flood forecasting can be helpful for people to take precaution.In the past decades, researchers have been working on flood forecasting models using artificial intelligence (AI).AI models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been developed and implemented in different locations to help in weather forecasting over the past years. This paper reviews both methods and compares their experimental results.
format Conference or Workshop Item
author Suliman, Azizah
Nazri, Nursyazana
Othman, Marini
Abdul Malek, Marlinda
Ku-Mahamud, Ku Ruhana
author_facet Suliman, Azizah
Nazri, Nursyazana
Othman, Marini
Abdul Malek, Marlinda
Ku-Mahamud, Ku Ruhana
author_sort Suliman, Azizah
title Artificial neural network and support vector machine in flood forecasting: A review
title_short Artificial neural network and support vector machine in flood forecasting: A review
title_full Artificial neural network and support vector machine in flood forecasting: A review
title_fullStr Artificial neural network and support vector machine in flood forecasting: A review
title_full_unstemmed Artificial neural network and support vector machine in flood forecasting: A review
title_sort artificial neural network and support vector machine in flood forecasting: a review
publishDate 2013
url http://repo.uum.edu.my/12033/1/PID30.pdf
http://repo.uum.edu.my/12033/
http://www.icoci.cms.net.my/proceedings/2013/TOC.html
_version_ 1644280802845917184
score 13.149126

類似資料