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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2024
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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 |
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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 |
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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 |
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1814061172740063232 |
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13.214268 |