Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling consi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf |
| _version_ | 1796993822125719552 |
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| author | Nurul Najihah, Che Razali Ngahzaifa, Ab. Ghani Syifak Izhar, Hisham Shahreen, Kasim Widodo, Nuryono Satya Sutikno, Tole |
| author_facet | Nurul Najihah, Che Razali Ngahzaifa, Ab. Ghani Syifak Izhar, Hisham Shahreen, Kasim Widodo, Nuryono Satya Sutikno, Tole |
| author_sort | Nurul Najihah, Che Razali |
| collection | UMP |
| description | This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the
problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m3/s). For model parameters, the models apply three triangle membership
functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square Error (RMSE). Models with good performance in training have low values of RMSE. Hence, the 4-input model data is the best model to measure prediction accurately with the value of RMSE as 22.157. It is proven that ANFIS has the potential to be used for flood forecasting generally, or rainfall-runoff modelling specifically. |
| first_indexed | 2024-03-06T12:39:13Z |
| format | Article |
| id | UMPir27154 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:39:13Z |
| publishDate | 2020 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | UMPir271542020-02-18T06:53:26Z http://umpir.ump.edu.my/id/eprint/27154/ Rainfall-runoff modelling using adaptive neuro-fuzzy inference system Nurul Najihah, Che Razali Ngahzaifa, Ab. Ghani Syifak Izhar, Hisham Shahreen, Kasim Widodo, Nuryono Satya Sutikno, Tole QA75 Electronic computers. Computer science This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m3/s). For model parameters, the models apply three triangle membership functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square Error (RMSE). Models with good performance in training have low values of RMSE. Hence, the 4-input model data is the best model to measure prediction accurately with the value of RMSE as 22.157. It is proven that ANFIS has the potential to be used for flood forecasting generally, or rainfall-runoff modelling specifically. Institute of Advanced Engineering and Science 2020-02 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf Nurul Najihah, Che Razali and Ngahzaifa, Ab. Ghani and Syifak Izhar, Hisham and Shahreen, Kasim and Widodo, Nuryono Satya and Sutikno, Tole (2020) Rainfall-runoff modelling using adaptive neuro-fuzzy inference system. Indonesian Journal of Electrical Engineering and Computer Science, 17 (2). pp. 1117-1126. ISSN 2502-4752. (Published) http://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126 http://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126 |
| spellingShingle | QA75 Electronic computers. Computer science Nurul Najihah, Che Razali Ngahzaifa, Ab. Ghani Syifak Izhar, Hisham Shahreen, Kasim Widodo, Nuryono Satya Sutikno, Tole Rainfall-runoff modelling using adaptive neuro-fuzzy inference system |
| title | Rainfall-runoff modelling using adaptive neuro-fuzzy inference system |
| title_full | Rainfall-runoff modelling using adaptive neuro-fuzzy inference system |
| title_fullStr | Rainfall-runoff modelling using adaptive neuro-fuzzy inference system |
| title_full_unstemmed | Rainfall-runoff modelling using adaptive neuro-fuzzy inference system |
| title_short | Rainfall-runoff modelling using adaptive neuro-fuzzy inference system |
| title_sort | rainfall runoff modelling using adaptive neuro fuzzy inference system |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf |
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