Metaheuristic Algorithms to Enhance the Performance of a Feedforward Neural Network in Addressing Missing Hourly Precipitation
This research study investigates the implementation of three metaheuristic algorithms, namely, Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO), and Moth-Flame Optimisation (MFO), for coupling with a Feedforward Neural Network (FNN) in addressing missing hourly rainfall observations, while ove...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTHM
2023
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/44780/1/Metaheuristic%20Algorithms.pdf http://ir.unimas.my/id/eprint/44780/ https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/8725 https://doi.org/10.30880/ijie.2023.15.01.025 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | This research study investigates the implementation of three metaheuristic algorithms, namely, Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO), and Moth-Flame Optimisation (MFO), for coupling with a Feedforward Neural Network (FNN) in addressing missing hourly rainfall observations, while overcoming the limitation of conventional training algorithm of artificial neural network that often traps in local optima. The proposed GWOFNN, MVOFNN, and MFOFNN were compared against the conventional Levenberg Marquardt
Feedforward Neural Network (LMFNN) in addressing the artificially introduced missing hourly rainfall records of
Kuching Third Mile Station. The findings show that the proposed approaches are superior to LMFNN in predicting
the 20% hourly rainfall observations in terms of mean absolute error (MAE) and coefficient of correlation (r). The
best performance ANN model is GWOFNN, followed with MVOFNN, MFOFNN and lastly LMFNN. |
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