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Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site

Author

Listed:
  • Basri Badyalina
  • Ani Shabri
  • Nurkhairany Amyra Mokhtar
  • Mohamad Faizal Ramli
  • Muhammad Majid
  • Muhammad Yassar Yusri

Abstract

Handling flood quantile with little data is essential in managing water resources. In this paper, we propose a potential model called Modified Group Method of Data Handling (MGMDH) to predict the flood quantile at ungauged sites in Malaysia. In this proposed MGMDH model, the principal component analysis (PCA) method is matched to the group method of data handling (GMDH) with various transfer functions. The MGMDH model consists of four transfer functions- polynomial, sigmoid, radial basis function, and hyperbolic tangent sigmoid transfer functions. The prediction performance of MGMDH models is compared to the conventional GMDH model. The appropriateness and effectiveness of the proposed models are demonstrated with a simulation study. Cauchy distribution is used in the simulation study as a disturbance error. The implementation of Cauchy Distribution as an error disturbance in artificial data illustrates the performance of the proposed models if the extreme value or extreme event occurs in the data set. The simulation study may say that the MGMDH model is superior to other comparison models, namely LR, NLR, GMDH and ANN models. Another beauty of this proposed model is that it shows a strong prediction performance when multicollinearity is absent in the data set.

Suggested Citation

  • Basri Badyalina & Ani Shabri & Nurkhairany Amyra Mokhtar & Mohamad Faizal Ramli & Muhammad Majid & Muhammad Yassar Yusri, 2021. "Modified Group Method of Data Handling for Flood Quantile Prediction at Ungauged Site," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(6), pages 1-57, December.
    • Handle: RePEc:ibn:ijspjl:v:10:y:2021:i:6:p:57
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    References listed on IDEAS

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    1. Sohel Rana & Habshah Midi & A. H. M. R. Imon, 2012. "Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-14, February.
    2. Barth, Jackson & Katumullage, Duwani & Yang, Chenyu & Cao, Jing, 2021. "Classification of Wines Using Principal Component Analysis," Journal of Wine Economics, Cambridge University Press, vol. 16(1), pages 56-67, February.
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    Cited by:

    1. Nor Hidayah Hassim & Basri Badyalina & Nurkhairany Amyra Mokhtar & Muhammad Zulqarnain Hakim Abd Jalal & Nur Diana Binti Zamani & Lee Chang Kerk & Amir Imran Zainoddin & Ahmad Syahmi Ahmad Fadzil & Nu, 2022. "In Situ Flood Frequency Analysis Used for Water Resource Management in Kelantan River Basin," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(5), pages 1-8, November.

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      JEL classification:

      • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
      • Z0 - Other Special Topics - - General

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