Ngahzaifa, Ab. Ghani and Zuriani, Mustaffa and Zafril Rizal, M Azmi (2018) Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset. Advanced Science Letters, 24 (10). pp. 7617-7622. ISSN 1936-6612. (Published)
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Abstract
ANFIS (Adaptive Neuro Fuzzy Inference System), for its advantages of having linguistic representation of models has been the interests of both hydrological operational modellers and scientists/theorists. In hydrology especially, every process is unique and dependent on large number of natural factors hence modelling using machine learning algorithm without considering hydrological insight is very dangerous. In using most of the machine learning algorithms including ANFIS, to obtain the best model, the common and normal approach is always by comparing models of different parameter settings based on the goodness-offit statistical measures. This approach is not always accurate, as each statistical measure has its drawbacks in terms of how accurate it is presenting the model depending on the condition and complexity of the data involved. This research proposes the use of synthetic data in order to explore and understand the behaviour of ANFIS parameters on hydrological data, specifically hysteresis effect in sediment-discharge relationship, in order to improve the efficiency of the modelling process. The results of simulation on the synthetic datasets are then presented and analysed visually. The modelling process is then repeated on real datasets in order to validate the findings.
| Item Type: | Article |
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| Additional Information: | JCR® Category: Multidisciplinary Sciences. Quartile: Q2 |
| Uncontrolled Keywords: | Machine learning; Neuro fuzzy; ANFIS; Sediment discharge; Hysteresis; Computing intelligence |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 06 Mar 2018 02:47 |
| Last Modified: | 22 Nov 2018 03:15 |
| URI: | http://umpir.ump.edu.my/id/eprint/19447 |
| Download Statistic: | View Download Statistics |
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