Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia
Joint Authors
Supatmi, Sri
Hou, Rongtao
Sumitra, Irfan Dwiguna
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-25
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS).
The study aims finding which approach gives the best performance for forecasting flood vulnerability.
Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs).
The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia.
Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.
American Psychological Association (APA)
Supatmi, Sri& Hou, Rongtao& Sumitra, Irfan Dwiguna. 2019. Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129533
Modern Language Association (MLA)
Supatmi, Sri…[et al.]. Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1129533
American Medical Association (AMA)
Supatmi, Sri& Hou, Rongtao& Sumitra, Irfan Dwiguna. Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129533
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129533