Statistical analysis for multiplicatively modulated nonlinear autoregressive model and its applications to electrophysiological signal analysis in humans

2006 ◽  
Vol 54 (9) ◽  
pp. 3414-3425 ◽  
Author(s):  
H. Kato ◽  
M. Taniguchi ◽  
M. Honda
Keyword(s):  
Statistical Analysis ◽  
Autoregressive Model ◽  
Signal Analysis ◽  
Nonlinear Autoregressive Model ◽  
Electrophysiological Signal ◽  
Nonlinear Autoregressive

scholarly journals Geomorphology-Wavelet based approach to rainfall runoff modeling for data scarce semi-arid regions, Kolar River catchment, India

2021 ◽  
Author(s):  
Deepak Kumar Tiwari ◽  
◽  
Tiwari H. L. ◽  
Raman Nateriya ◽  
◽  
...  
Keyword(s):  
Autoregressive Model ◽  
Water Resource Management ◽  
Kendall Test ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Slope Test ◽  
Runoff Modeling ◽  
Nonlinear Autoregressive Model ◽  
Nonlinear Autoregressive ◽  
Semi Arid

The conceptual and physical mathematical model of rainfall-runoff modeling uses various parameters such as land use land cover, soil type classification, rainfall, atmospheric data such as temperature, evapotranspiration, solar radiation and wind speed, etc. But these data may not be available for developing countries and data scares semi-arid watershed. Also, the problem is even more critical for ungauged catchments and where manual record is maintained of water level and rainfall data. To address this issue, trend analysis is performed using Mann-Kendall test and Sen’s slope test which shows significant trend change stressing the need for new method for runoff prediction for better water resource management. In this study, a total of four models namely nonlinear autoregressive model with exogenous inputs lumped (LNARX), nonlinear autoregressive model with exogenous geomorphometrically processed inputs (GNARX), wavelet nonlinear autoregressive model with exogenous inputs (WLNARX) and nonlinear autoregressive model with exogenous geomorphometrically processed inputs (WGNARX). Ten models with different input combinations were selected based on their performance are analyzed for all the four networks. The best performing model for these networks is model no. 6 with WGNARX network with NSE 0.97 and RMSE 0.97 and with least value of RMSE. This method can be applied to data scarce region where data available are available for shorter duration and helpful for ungauged catchments also.

Sign in / Sign up

Export Citation Format

Share Document