IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2696-d663497.html
   My bibliography  Save this article

Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines

Author

Listed:
  • Nawin Raj

    (School of Sciences, Springfield Campus, University of Southern Queensland, Toowoomba, QLD 4300, Australia)

  • Zahra Gharineiat

    (School of Civil Engineering and Surveying, Springfield Campus, University of Southern Queensland, Toowoomba, QLD 4300, Australia)

Abstract

Mean sea level rise is a significant emerging risk from climate change. This research paper is based on the use of artificial intelligence models to assess and predict the trend on mean sea level around northern Australian coastlines. The study uses sea-level times series from four sites (Broom, Darwin, Cape Ferguson, Rosslyn Bay) to make the prediction. Multivariate adaptive regression splines (MARS) and artificial neural network (ANN) algorithms have been implemented to build the prediction model. Both models show high accuracy (R 2 > 0.98) and low error values (RMSE < 27%) overall. The ANN model showed slightly better performance compared to MARS over the selected sites. The ANN performance was further assessed for modelling storm surges associated with cyclones. The model reproduced the surge profile with the maximum correlation coefficients ~0.99 and minimum RMS errors ~4 cm at selected validating sites. In addition, the ANN model predicted the maximum surge at Rosslyn Bay for cyclone Marcia to within 2 cm of the measured peak and the maximum surge at Broome for cyclone Narelle to within 7 cm of the measured peak. The results are comparable with a MARS model previously used in this region; however, the ANN shows better agreement with the measured peak and arrival time, although it suffers from slightly higher predictions than the observed sea level by tide gauge station.

Suggested Citation

  • Nawin Raj & Zahra Gharineiat, 2021. "Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2696-:d:663497
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2696/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/21/2696/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schwertman, Neil C. & Owens, Margaret Ann & Adnan, Robiah, 2004. "A simple more general boxplot method for identifying outliers," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 165-174, August.
    2. Dégerine, Serge & Lambert-Lacroix, Sophie, 2003. "Characterization of the partial autocorrelation function of nonstationary time series," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 46-59, October.
    3. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. José A. Sáez & José L. Romero-Béjar, 2022. "Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation," Mathematics, MDPI, vol. 10(14), pages 1-14, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mostafa Esmaeili Shayan & Gholamhassan Najafi & Barat Ghobadian & Shiva Gorjian & Mohamed Mazlan & Mehdi Samami & Alireza Shabanzadeh, 2022. "Flexible Photovoltaic System on Non-Conventional Surfaces: A Techno-Economic Analysis," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    2. Feiyan Chen & Zhigao Zhou & Aiwen Lin & Jiqiang Niu & Wenmin Qin & Zhong Yang, 2019. "Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods," Energies, MDPI, vol. 12(1), pages 1-19, January.
    3. Shengdi Chen & Qingwen Xue & Xiaochen Zhao & Yingying Xing & Jian John Lu, 2021. "Risky Driving Behavior Recognition Based on Vehicle Trajectory," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    4. Yaohui Li & Junjun Shi & Zhifeng Yin & Jingfang Shen & Yizhong Wu & Shuting Wang, 2021. "An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
    5. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    6. Feng, Lan & Lin, Aiwen & Wang, Lunche & Qin, Wenmin & Gong, Wei, 2018. "Evaluation of sunshine-based models for predicting diffuse solar radiation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 168-182.
    7. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    8. Gu, Bingmei & Liu, Jiaguo & Ye, Xiaoheng & Gong, Yu & Chen, Jihong, 2024. "Data-driven approach for port resilience evaluation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    9. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    10. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    11. Lu Chen & Luca Sartore & Habtamu Benecha & Valbona Bejleri & Balgobin Nandram, 2022. "Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs," Stats, MDPI, vol. 5(3), pages 1-18, September.
    12. Junjun Shi & Jingfang Shen & Yaohui Li, 2021. "High-Precision Kriging Modeling Method Based on Hybrid Sampling Criteria," Mathematics, MDPI, vol. 9(5), pages 1-25, March.
    13. Jianguo Zhou & Qiqi Wang, 2021. "Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    14. Babek Erdebilli & Burcu Devrim-İçtenbaş, 2022. "Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    15. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    16. Fei Ma & Fei Liu & Qipeng Sun & Wenlin Wang & Xiaodan Li, 2018. "Measuring and Spatio-Temporal Evolution for the Late-Development Advantage in China’s Provinces," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    17. Duan, Qiuhua & Feng, Yanxiao & Wang, Julian, 2021. "Clustering of visible and infrared solar irradiance for solar architecture design and analysis," Renewable Energy, Elsevier, vol. 165(P1), pages 668-677.
    18. Ataee, Sadegh & Ameri, Mehran & Askari, Ighball Baniasad & Keshtegar, Behrooz, 2024. "Evaluation and intelligent forecasting of energy and exergy efficiencies of a nanofluid-based filled-type U-pipe solar ETC using three machine learning approaches," Energy, Elsevier, vol. 298(C).
    19. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    20. Kisi, Ozgur & Heddam, Salim & Yaseen, Zaher Mundher, 2019. "The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model," Applied Energy, Elsevier, vol. 241(C), pages 184-195.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2696-:d:663497. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.