Cover Image

Review of flood prediction hybrid machine learning models using datasets

Floods are among the most destructive natural disasters, and they are extremely difficult to model. Over the last two decades, machine learning (ML) methods have made significant contributions to the advancement of prediction systems that provide better performance and cost-effective solutions by mi...

Full description

Saved in:
Bibliographic Details
Main Authors: Zuhairi, Ainaa Hanis, Yakub, Fitri, Zaki, Sheikh Ahmad, Mat Ali, Mohamed Sukri
Format: Conference or Workshop Item
Language:English
Published: 2022
Subjects:
QA Mathematics
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/98995/1/AinaaHanisZuhairi2022_ReviewofFloodPredictionHybridMachine.pdf
http://eprints.utm.my/id/eprint/98995/
http://dx.doi.org/10.1088/1755-1315/1091/1/012040
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.98995
record_format eprints
spelling my.utm.989952023-02-22T03:34:05Z http://eprints.utm.my/id/eprint/98995/ Review of flood prediction hybrid machine learning models using datasets Zuhairi, Ainaa Hanis Yakub, Fitri Zaki, Sheikh Ahmad Mat Ali, Mohamed Sukri QA Mathematics TA Engineering (General). Civil engineering (General) Floods are among the most destructive natural disasters, and they are extremely difficult to model. Over the last two decades, machine learning (ML) methods have made significant contributions to the advancement of prediction systems that provide better performance and cost-effective solutions by mimicking the complex mathematical expressions of physical flood processes. Because of the numerous benefits and potential of ML, its popularity has skyrocketed. Researchers hope to discover more accurate and efficient prediction models by introducing novel ML methods and hybridising existing ones. The main focus of this paper is to show the state of the art of hybridising ML models in flood prediction. The most effective strategies for improving ML methods are hybridization, data decomposition, algorithm ensemble, and model optimization. 2022-12 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/98995/1/AinaaHanisZuhairi2022_ReviewofFloodPredictionHybridMachine.pdf Zuhairi, Ainaa Hanis and Yakub, Fitri and Zaki, Sheikh Ahmad and Mat Ali, Mohamed Sukri (2022) Review of flood prediction hybrid machine learning models using datasets. In: 9th AUN/SEED-Net Regional Conference on Natural Disaster, RCND 2021, 15 December 2021 - 16 December 2021, Virtual, Online. http://dx.doi.org/10.1088/1755-1315/1091/1/012040
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 QA Mathematics
TA Engineering (General). Civil engineering (General)
spellingShingle QA Mathematics
TA Engineering (General). Civil engineering (General)
Zuhairi, Ainaa Hanis
Yakub, Fitri
Zaki, Sheikh Ahmad
Mat Ali, Mohamed Sukri
Review of flood prediction hybrid machine learning models using datasets
description Floods are among the most destructive natural disasters, and they are extremely difficult to model. Over the last two decades, machine learning (ML) methods have made significant contributions to the advancement of prediction systems that provide better performance and cost-effective solutions by mimicking the complex mathematical expressions of physical flood processes. Because of the numerous benefits and potential of ML, its popularity has skyrocketed. Researchers hope to discover more accurate and efficient prediction models by introducing novel ML methods and hybridising existing ones. The main focus of this paper is to show the state of the art of hybridising ML models in flood prediction. The most effective strategies for improving ML methods are hybridization, data decomposition, algorithm ensemble, and model optimization.
format Conference or Workshop Item
author Zuhairi, Ainaa Hanis
Yakub, Fitri
Zaki, Sheikh Ahmad
Mat Ali, Mohamed Sukri
author_facet Zuhairi, Ainaa Hanis
Yakub, Fitri
Zaki, Sheikh Ahmad
Mat Ali, Mohamed Sukri
author_sort Zuhairi, Ainaa Hanis
title Review of flood prediction hybrid machine learning models using datasets
title_short Review of flood prediction hybrid machine learning models using datasets
title_full Review of flood prediction hybrid machine learning models using datasets
title_fullStr Review of flood prediction hybrid machine learning models using datasets
title_full_unstemmed Review of flood prediction hybrid machine learning models using datasets
title_sort review of flood prediction hybrid machine learning models using datasets
publishDate 2022
url http://eprints.utm.my/id/eprint/98995/1/AinaaHanisZuhairi2022_ReviewofFloodPredictionHybridMachine.pdf
http://eprints.utm.my/id/eprint/98995/
http://dx.doi.org/10.1088/1755-1315/1091/1/012040
_version_ 1758578048469303296
score 13.160551

Similar Items