This paper is published in Volume-4, Issue-1, 2018
Area
Soil and Water Conservation Engineering
Author
Ashish Sachan, Dr. Devendra Kumar
Org/Univ
Govind Ballabh Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India
Pub. Date
02 February, 2018
Paper ID
V4I1-1305
Publisher
Keywords
Runoff, Watershed, Hybrid Model, Wavelet, ANFIS, etc.

Citationsacebook

IEEE
Ashish Sachan, Dr. Devendra Kumar. Neural Fuzzy Inference System Modelling with Different Input Vectors for Rainfall-Runoff Prediction, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Ashish Sachan, Dr. Devendra Kumar (2018). Neural Fuzzy Inference System Modelling with Different Input Vectors for Rainfall-Runoff Prediction. International Journal of Advance Research, Ideas and Innovations in Technology, 4(1) www.IJARIIT.com.

MLA
Ashish Sachan, Dr. Devendra Kumar. "Neural Fuzzy Inference System Modelling with Different Input Vectors for Rainfall-Runoff Prediction." International Journal of Advance Research, Ideas and Innovations in Technology 4.1 (2018). www.IJARIIT.com.

Abstract

A convenient and acceptable technique to develop mathematical models is through conceptual formulation and statistical development while integrating the effects of various variables on these physical processes.  Overemphasizing on these techniques could result in an increase in complexity of model which in turn influence the performance of the model. In this study, one conjunction model combining wavelet-neuro-fuzzy for runoff forecast is proposed and compared with simple neuro-fuzzy inference system. The inflow series to the conjunction model has been decomposed by wavelet transform. The performance of the conjunction model under the changed inflow parameters has been compared with the simple model. The results show that both the model performed well, however, increase in complexity of a model does not necessarily increase the performance of the model.