Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

Isa Ebtehaj, Saad Sh Sammen, Lariyah Mohd Sidek, Anurag Malik, Parveen Sihag, Ahmed Mohammed Sami Al-Janabi, Kwok Wing Chau, Hossein Bonakdari

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


Accurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method of Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict the daily WL at Telom and Bertam stations located in Cameron Highlands of Malaysia. Different percentage ratio for data division i.e. 50%–50% (scenario-1), 60%–40% (scenario-2), and 70%–30% (scenario-3) were adopted for training and testing of these models. To show the efficiency of the proposed hybrid models, their results were compared with the standalone models that include the Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH). The results of the investigation revealed that the hybrid GS-GMDH and ANFIS-FCM models outperformed the standalone GEP and GMDH models for the prediction of daily WL at both study sites. In addition, the results indicate the best performance for WL prediction was obtained in scenario-3 (70%–30%). In summary, the results highlight the better suitability and supremacy of the proposed hybrid GS-GMDH and ANFIS-FCM models in daily WL prediction, and can, serve as robust and reliable predictive tools for the study region.

Original languageEnglish
Pages (from-to)1343-1361
Number of pages19
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - Sep 2021


  • Cameron highland
  • GEP
  • GMDH
  • hybrid models
  • Water level prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation


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