Open AccessRainfall-runoff modelling using adaptive neuro-fuzzy inference system
Author(s) -
Nurul Najihah Che Razali,
Ngahzaifa Ab Ghani,
Syifak Izhar Hisham,
Shahreen Kasim,
Nuryono Satya Widodo,
Tole Sutikno
Publication year - 2020
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v17.i2.pp1117-1126
Subject(s) - adaptive neuro fuzzy inference system , mean squared error , surface runoff , flood myth , measure (data warehouse) , statistics , computer science , environmental science , hydrology (agriculture) , mathematics , fuzzy logic , meteorology , data mining , engineering , fuzzy control system , artificial intelligence , geotechnical engineering , geography , ecology , archaeology , biology
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 m 3 /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.