Cover Image

Deep learning neural network for time series water level forecasting

Deep neural networks; Flood control; Forecasting; Learning systems; Long short-term memory; Mean square error; Offshore oil well production; Time series; Water levels; Coefficient of determination; Daily time series; Forecasting models; Learning neural networks; Learning techniques; Root mean square...

Full description

Saved in:
Bibliographic Details
Main Authors: Zaini N., Malek M.A., Norhisham S., Mardi N.H.
Other Authors: 56905328500
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-26584
record_format dspace
spelling my.uniten.dspace-265842023-05-29T17:12:19Z Deep learning neural network for time series water level forecasting Zaini N. Malek M.A. Norhisham S. Mardi N.H. 56905328500 55636320055 54581400300 57190171141 Deep neural networks; Flood control; Forecasting; Learning systems; Long short-term memory; Mean square error; Offshore oil well production; Time series; Water levels; Coefficient of determination; Daily time series; Forecasting models; Learning neural networks; Learning techniques; Root mean square errors; Training and testing; Water level forecasting; Deep learning Reliable forecasting of water level is essential for flood prevention, future planning and warning. This study proposed to forecast daily time series water level for Malaysia�s rivers based on deep learning technique namely long short-term memory (LSTM). The deep learning neural network is based on artificial neural network (ANN) and part of broader machine learning. In this study, forecasting models are developed for 1-h ahead of time at multiple lag time which are 1-h, 2-h and 3-h lag time denoted as LSTMt-1, LSTMt-2 and LSTMt-3, respectively. Forecasted water level is significant for determination of effected area, future planning and warning. Root mean square error (RMSE) and coefficient of determination (R2 ) are utilized to evaluate the performance of proposed forecasting models. An analysis of error in term of RMSE and R2 show that the proposed LSTMt-3 model outperformed other models for water level forecasting during training and testing phase. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. Final 2023-05-29T09:12:18Z 2023-05-29T09:12:18Z 2021 Conference Paper 10.1007/978-981-33-6311-3_3 2-s2.0-85100739606 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100739606&doi=10.1007%2f978-981-33-6311-3_3&partnerID=40&md5=ff2f3f59d26129037af4175a0e2be86b https://irepository.uniten.edu.my/handle/123456789/26584 132 22 29 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Deep neural networks; Flood control; Forecasting; Learning systems; Long short-term memory; Mean square error; Offshore oil well production; Time series; Water levels; Coefficient of determination; Daily time series; Forecasting models; Learning neural networks; Learning techniques; Root mean square errors; Training and testing; Water level forecasting; Deep learning
author2 56905328500
author_facet 56905328500
Zaini N.
Malek M.A.
Norhisham S.
Mardi N.H.
format Conference Paper
author Zaini N.
Malek M.A.
Norhisham S.
Mardi N.H.
spellingShingle Zaini N.
Malek M.A.
Norhisham S.
Mardi N.H.
Deep learning neural network for time series water level forecasting
author_sort Zaini N.
title Deep learning neural network for time series water level forecasting
title_short Deep learning neural network for time series water level forecasting
title_full Deep learning neural network for time series water level forecasting
title_fullStr Deep learning neural network for time series water level forecasting
title_full_unstemmed Deep learning neural network for time series water level forecasting
title_sort deep learning neural network for time series water level forecasting
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806427750514294784
score 13.222552

Similar Items