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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Predicting Reservoir Water Level

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dc.citedby10
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorSheikh Khozani Z.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57185668800en_US
dc.contributor.authorid35070506500en_US
dc.date.accessioned2024-10-14T03:18:54Z
dc.date.available2024-10-14T03:18:54Z
dc.date.issued2023
dc.description.abstractPredicting reservoir water levels helps manage droughts and floods. Predicting reservoir water level is complex because it depends on factors such as climate parameters and human intervention. Therefore, predicting water level needs robust models. Our study introduces a new model for predicting reservoir water levels. An extreme learning machine, the multi-kernel least square support vector machine model (MKLSSVM), is developed to predict the water level of a reservoir in Malaysia. The study also introduces a novel optimization algorithm for selecting inputs. While the LSSVM model may not capture nonlinear components of the time series data, the extreme learning machine (ELM) model�MKLSSVM model can capture nonlinear and linear components of the time series data. A coati optimization algorithm is introduced to select input scenarios. The MKLSSVM model takes advantage of multiple kernel functions. The extreme learning machine model�multi-kernel least square support vector machine model also takes the benefit of both the ELM model and MKLSSVM model models to predict water levels. This paper�s novelty includes introducing a new method for selecting inputs and developing a new model for predicting water levels. For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. The testing means absolute of the same models was 0.710, 0.742, 0.832, 0.871, 0.912, and 0.919, respectively. The Nash�Sutcliff efficiency (NSE) testing of the same models was 0.97, 0.94, 0.90, 0.87, 0.83, and 0.18, respectively. The ELM-MKLSSVM model is a robust tool for predicting reservoir water levels. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1593
dc.identifier.doi10.3390/w15081593
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85156194074
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85156194074&doi=10.3390%2fw15081593&partnerID=40&md5=1e77fed9e0e2ba3ffbdee0d0a791cc2b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34297
dc.identifier.volume15
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.subjecthybrid models
dc.subjecthydrological simulations
dc.subjectoptimization algorithms
dc.subjectwater level
dc.subjectMalaysia
dc.subjectForecasting
dc.subjectKnowledge acquisition
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMachine components
dc.subjectOptimization
dc.subjectRadial basis function networks
dc.subjectReservoirs (water)
dc.subjectSupport vector machines
dc.subjectTime series
dc.subjectVectors
dc.subjectHybrid model
dc.subjectHydrological simulations
dc.subjectKernel function
dc.subjectLearning machines
dc.subjectLeast square support vector machines
dc.subjectMachine modelling
dc.subjectMulti-kernel
dc.subjectOptimization algorithms
dc.subjectReservoir water level
dc.subjectSupport vector machine models
dc.subjectalgorithm
dc.subjecthydrological modeling
dc.subjectmachine learning
dc.subjectoptimization
dc.subjectrainfall
dc.subjectreservoir
dc.subjectsupport vector machine
dc.subjectwater level
dc.subjectWater levels
dc.titleBinary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Predicting Reservoir Water Levelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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