A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India.

Reliable and accurate modelling of streamflow is still a challenging task due to their complex behaviour, need for extensive parameter for development as well as lack of complete or accurate data. In this study, the applicability of an emerging data-driven model, specifically a neural network autoregression (NNAR) model, was evaluated for the first time as a substitute to the physically based hydrological model Soil and Water Assessment Tool (SWAT) for predicting streamflow under data-scarce conditions and for immediate high-quality modelling results. The inputs to the NNAR model were the lagged values of the daily streamflow time series data, and the output was the predicted value for the next day. Using streamflow data that was windowed by 20 days, the NNAR model produced the best prediction. The results of the statistical metrics used to evaluate the performance of the NNAR model were satisfactory (R = 0.90, RMSE = 28.27, MAE = 11.92, R2 = 0.83), indicating a high degree of agreement between the predicted and observed streamflow. The NNAR model outputs demonstrated its ability to accurately predict streamflow in the river basin, even without an explicit understanding of the physical processes that govern the system.

Subspace techniques for task-independent EEG person identification.

There has been a growing interest in studying electroencephalography signals (EEG) as a possible biometric. The brain signals captured by EEG are rich and carry information related to the individual, tasks being performed, mental state, and other channel/measurement noise due to session variability and artifacts. To effectively extract person-specific signatures present in EEG, it is necessary to define a subspace that enhances the biometric information and suppresses other nuisance factors. i-vector and x-vector are state-of-art subspace techniques used in speaker recognition. In this paper, novel modifications are proposed for both frameworks to project person-specific signatures from multi-channel EEG into a subspace. The modified i-vector and x-vector systems outperform baseline i-vector and x-vector systems with an absolute improvement of 10.5% and 15.9%, respectively.