Authors: Sri Supatmi, Rongtao Hou, Irfan Dwiguna Sumitra
DOI: 10.1155/2019/6203510
Abstract
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. © 2019 Sri Supatmi et al.
Indexed keywords
MeSH
Algorithms; Computer Simulation; Floods; Forecasting; Fuzzy Logic; Humans; Indonesia
Engineering controlled terms
Flood control; Floods; Fuzzy inference; Fuzzy systems; Membership functions
Engineering uncontrolled terms
Experimental investigations; Flood forecasting models; Flood vulnerabilities; Forecasting accuracy; Fuzzy inference systems; Mamdani fuzzy inferences; Neuro-fuzzy inference systems; Research results
EMTREE medical terms
algorithm; computer simulation; flooding; forecasting; fuzzy logic; human; Indonesia; procedures
Engineering main heading
Weather forecasting
This article can be accessed at https://www.scopus.com/pages/publications/85062826333
