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
Predicting 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 rese...
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my.uniten.dspace-342972024-10-14T11:18:54Z 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 Sammen S.S. Ehteram M. Sheikh Khozani Z. Sidek L.M. 57192093108 57113510800 57185668800 35070506500 hybrid models hydrological simulations optimization algorithms water level Malaysia Forecasting Knowledge acquisition Learning algorithms Learning systems Machine components Optimization Radial basis function networks Reservoirs (water) Support vector machines Time series Vectors Hybrid model Hydrological simulations Kernel function Learning machines Least square support vector machines Machine modelling Multi-kernel Optimization algorithms Reservoir water level Support vector machine models algorithm hydrological modeling machine learning optimization rainfall reservoir support vector machine water level Water levels Predicting 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. Final 2024-10-14T03:18:54Z 2024-10-14T03:18:54Z 2023 Article 10.3390/w15081593 2-s2.0-85156194074 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156194074&doi=10.3390%2fw15081593&partnerID=40&md5=1e77fed9e0e2ba3ffbdee0d0a791cc2b https://irepository.uniten.edu.my/handle/123456789/34297 15 8 1593 All Open Access Gold Open Access MDPI Scopus |
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hybrid models hydrological simulations optimization algorithms water level Malaysia Forecasting Knowledge acquisition Learning algorithms Learning systems Machine components Optimization Radial basis function networks Reservoirs (water) Support vector machines Time series Vectors Hybrid model Hydrological simulations Kernel function Learning machines Least square support vector machines Machine modelling Multi-kernel Optimization algorithms Reservoir water level Support vector machine models algorithm hydrological modeling machine learning optimization rainfall reservoir support vector machine water level Water levels |
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hybrid models hydrological simulations optimization algorithms water level Malaysia Forecasting Knowledge acquisition Learning algorithms Learning systems Machine components Optimization Radial basis function networks Reservoirs (water) Support vector machines Time series Vectors Hybrid model Hydrological simulations Kernel function Learning machines Least square support vector machines Machine modelling Multi-kernel Optimization algorithms Reservoir water level Support vector machine models algorithm hydrological modeling machine learning optimization rainfall reservoir support vector machine water level Water levels Sammen S.S. Ehteram M. Sheikh Khozani Z. Sidek L.M. 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 |
| description |
Predicting 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. |
| author2 |
57192093108 |
| author_facet |
57192093108 Sammen S.S. Ehteram M. Sheikh Khozani Z. Sidek L.M. |
| format |
Article |
| author |
Sammen S.S. Ehteram M. Sheikh Khozani Z. Sidek L.M. |
| author_sort |
Sammen S.S. |
| title |
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 |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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 |
| publisher |
MDPI |
| publishDate |
2024 |
| _version_ |
1814061174152495104 |
| score |
13.222552 |