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...
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| Main Authors: | , , , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
Institute of Advanced Engineering and Science
2020
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| 主題: | |
| 在線閱讀: | http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf http://umpir.ump.edu.my/id/eprint/27154/ http://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126 http://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126 |
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| 機構: | Universiti Malaysia Pahang Al-Sultan Abdullah |
| 語言: | English |
| 總結: | 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. |
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