Publication:
Application of Machine Learning for Daily Forecasting Dam Water Levels

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dc.citedby0
dc.contributor.authorAlmubaidinen_US
dc.contributor.authorAhmeden_US
dc.contributor.authorWinston C.A.A.en_US
dc.contributor.authorEl-Shajie A.en_US
dc.contributor.authorid57476845900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid59184867400en_US
dc.contributor.authorid59185623400en_US
dc.date.accessioned2024-10-14T03:17:21Z
dc.date.available2024-10-14T03:17:21Z
dc.date.issued2023
dc.description.abstractThe evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. Multiple studies have shown that machine learning, a data-driven approach to forecast hydrological processes, brings about more reliable data and is more efficient than traditional models. In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. Multiple input combinations were investigated to improve the model�s sensitivity, and statistical indicators were used to assess the reliability of the developed model. The study of multiple models with multiple input scenarios suggested that the bagged trees model trained with seven days of lagged input provided the highest accuracy. The bagged tree model achieved an RMSE of 0.13953, taking less than 10 seconds to train. Its efficiency and accuracy made this model stand out from the rest of the trained model. With the deployment of this model on the field, the dam water level predictions can be made to help mitigate issues relating to water supply. � 2023, Tikrit University. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.25130/tjes.30.4.9
dc.identifier.epage87
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85196736951
dc.identifier.spage74
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85196736951&doi=10.25130%2ftjes.30.4.9&partnerID=40&md5=36deb8de2decbcea7dd69a7af99d4463
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33854
dc.identifier.volume30
dc.pagecount13
dc.publisherTikrit Universityen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleTikrit Journal of Engineering Sciences
dc.subjectBagged Tree Model
dc.subjectMachine Learning
dc.subjectPredictions
dc.subjectWater Levels
dc.subjectWater Supply
dc.titleApplication of Machine Learning for Daily Forecasting Dam Water Levelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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