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

Mohammed Sami Al-Janabi, Ahmed (2021) Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models. Engineering Applications of Computational Fluid Mechanics, 15 (1).

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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

Item Type: Article
Uncontrolled Keywords: Water level prediction; hybrid models; GEP; GMDH; Cameron highland
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: ePrints deposit
Date Deposited: 13 Feb 2022 13:59
Last Modified: 13 Feb 2022 13:59

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