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Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia

Author

Listed:
  • T. Olivia Muslim

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Jalan Ikram-UNITEN, Kajang 43000, Selangor Darul Ehsan, Malaysia)

  • Ali Najah Ahmed

    (Institute for Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor Darul Ehsan, Malaysia)

  • M. A. Malek

    (Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor Darul Ehsan, Malaysia)

  • Haitham Abdulmohsin Afan

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Rusul Khaleel Ibrahim

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur 50603, Malaysia)

  • Amr El-Shafie

    (Civil Engineering Department, Giza High Institute for Engineering and Technology, Giza 12611, Egypt)

  • Michelle Sapitang

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Jalan Ikram-UNITEN, Kajang 43000, Selangor Darul Ehsan, Malaysia)

  • Mohsen Sherif

    (Water Research Center, United Arab Emirate University, Al Ain 15551, UAE)

  • Ahmed Sefelnasr

    (Water Research Center, United Arab Emirate University, Al Ain 15551, UAE)

  • Ahmed El-Shafie

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur 50603, Malaysia)

Abstract

This study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons—2019, 2023, 2028, 2048, and 2068—at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data.

Suggested Citation

  • T. Olivia Muslim & Ali Najah Ahmed & M. A. Malek & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Amr El-Shafie & Michelle Sapitang & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Investigating the Influence of Meteorological Parameters on the Accuracy of Sea-Level Prediction Models in Sabah, Malaysia," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:1193-:d:317691
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    References listed on IDEAS

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    1. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
    2. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
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    1. Sedigheh Mohamadi & Saad Sh. Sammen & Fatemeh Panahi & Mohammad Ehteram & Ozgur Kisi & Amir Mosavi & Ali Najah Ahmed & Ahmed El-Shafie & Nadhir Al-Ansari, 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 537-579, October.
    2. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.

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