Flood Forecasting Using Adaptive Neuro - Fuzzy Inference System

Authors

  • Bhavik Amin Water Resources Engineering , Civil Engineering Department L.D College Of EnginerringAhmedabad, India
  • Dr. R.B.Khasiya Prof. Water Resources Engineering, Civil Engineering Department L.D College ofEngineeringAhmedabad, India

Keywords:

anfis, river discharge, rmse

Abstract

This paper present the application of a data driven model, Flood Forecasting Using Adaptive Neuro – Fuzzy
Inference System in forecasting flood flow in Tapi river system ANFIS uses neural network algorithms and fuzzy
reasoning to map an input to an output space .The proposed technique combine the learning ability of neural network
with the transparent linguistic representation of fuzzy system. Performance of the ANFIS model with selected category
and membership function are tested and verified by applying daily rainfall and daily discharge data. Statistical indices
such as Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R2) and
Discrepancy Ratio (D) are used to evaluate performance of the ANFIS models in forecasting flood. This objective is
accomplished by evaluating the model by comparing ANFIS model to Statistical method like Log Pearson type-III
method to forecasting flood. This comparison shows that ANFIS model can accurately and reliably be used to forecast
flood in this study.

Published

2017-04-25

How to Cite

Bhavik Amin, & Dr. R.B.Khasiya. (2017). Flood Forecasting Using Adaptive Neuro - Fuzzy Inference System. International Journal of Advance Engineering and Research Development (IJAERD), 4(4), 210–214. Retrieved from https://ijaerd.com/index.php/IJAERD/article/view/2493