scholarly journals Application of artificial neural network and Soil and Water Assessment Tools in evaluating power generation of small hydropower stations

2020 ◽  
Vol 6 ◽  
pp. 2106-2118
Author(s):  
Xiaowen Cai ◽  
Feng Ye ◽  
Fatemeh Gholinia
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Artificial Neural Network ◽  
Assessment Tools ◽  
Small Hydropower ◽  
Artificial Neural ◽  
Hydropower Stations ◽  
Water Assessment ◽  
Soil And Water

STREAMFLOW SIMULATION: COMPARISON BETWEEN SOIL WATER ASSESSMENT TOOL AND ARTIFICIAL NEURAL NETWORK MODELS

2021 ◽  
Vol 5 (2) ◽  
pp. 173-182
Author(s):  
Shehu Usman Haruna ◽  
Aliyu Kasim Abba ◽  
Rabi'u Aminu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Water ◽  
Assessment Tool ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Artificial Neural ◽  
Soil Water Assessment Tool ◽  
Water Assessment ◽  
Streamflow Simulation

The present study compared the performance of two different models for streamflow simulation namely: Soil Water Assessment Tool (SWAT) and the Artificial Neural Network (ANN). During the calibration periods, the Nash-Sutcliff (NS) and Coefficient of Determination (R2) for SWAT was 0.74 and 0.81 respectively, whereas for ANN, it was 0.99 and 0.85 respectively. The ANN performs better during the validation period as the result revealed with NS and R2 having 0.98 and 0.89 respectively, while for the SWAT model it was 0.71 and 0.74 respectively. Based on the recommended comparison of graphical and statistical evaluation performances of both models, the ANN model performed better in estimating peak flow events than the SWAT model in the Upper Betwa Basin. Furthermore, the rigorous time required and expertise for calibration of the SWAT is much less as compared with the ANN. Moreover, the results obtained from both models demonstrate the performances of the

Sign in / Sign up

Export Citation Format

Share Document