Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management

As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources

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Main Authors: Latif S.D., Ahmed A.N.
Other Authors: 57216081524
Format: Article
Published: Springer Science and Business Media B.V. 2024
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spelling my.uniten.dspace-342392024-10-14T11:18:35Z Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management Latif S.D. Ahmed A.N. 57216081524 57214837520 Boosted regression tree (BRT) Dokan dam Long short-term memory (LSTM) Reservoir inflow Brain Climate change Economics Errors Flood control Learning algorithms Learning systems Mean square error Reservoirs (water) Water conservation Water management Water supply Boosted regression tree Boosted regression trees Dokan dam Long short-term memory Machine learning algorithms Reservoir inflow Root mean square errors Streamflow prediction Sustainable water supply Water supply management regression analysis reservoir sustainable development water management water resource water supply Long short-term memory As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources they have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy. � 2023, The Author(s), under exclusive licence to Springer Nature B.V. Final 2024-10-14T03:18:35Z 2024-10-14T03:18:35Z 2023 Article 10.1007/s11269-023-03499-9 2-s2.0-85150457950 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150457950&doi=10.1007%2fs11269-023-03499-9&partnerID=40&md5=0f69e50540855df76ef5bdddef7e2d98 https://irepository.uniten.edu.my/handle/123456789/34239 37 8 3227 3241 Springer Science and Business Media B.V. 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/
topic Boosted regression tree (BRT)
Dokan dam
Long short-term memory (LSTM)
Reservoir inflow
Brain
Climate change
Economics
Errors
Flood control
Learning algorithms
Learning systems
Mean square error
Reservoirs (water)
Water conservation
Water management
Water supply
Boosted regression tree
Boosted regression trees
Dokan dam
Long short-term memory
Machine learning algorithms
Reservoir inflow
Root mean square errors
Streamflow prediction
Sustainable water supply
Water supply management
regression analysis
reservoir
sustainable development
water management
water resource
water supply
Long short-term memory
spellingShingle Boosted regression tree (BRT)
Dokan dam
Long short-term memory (LSTM)
Reservoir inflow
Brain
Climate change
Economics
Errors
Flood control
Learning algorithms
Learning systems
Mean square error
Reservoirs (water)
Water conservation
Water management
Water supply
Boosted regression tree
Boosted regression trees
Dokan dam
Long short-term memory
Machine learning algorithms
Reservoir inflow
Root mean square errors
Streamflow prediction
Sustainable water supply
Water supply management
regression analysis
reservoir
sustainable development
water management
water resource
water supply
Long short-term memory
Latif S.D.
Ahmed A.N.
Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
description As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources
author2 57216081524
author_facet 57216081524
Latif S.D.
Ahmed A.N.
format Article
author Latif S.D.
Ahmed A.N.
author_sort Latif S.D.
title Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
title_short Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
title_full Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
title_fullStr Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
title_full_unstemmed Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
title_sort streamflow prediction utilizing deep learning and machine learning algorithms for sustainable water supply management
publisher Springer Science and Business Media B.V.
publishDate 2024
_version_ 1814061172740063232
score 13.209306