Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

2017 ◽  
Vol 136 ◽  
pp. 71-78 ◽  
Author(s):  
Yu Feng ◽  
Yong Peng ◽  
Ningbo Cui ◽  
Daozhi Gong ◽  
Kuandi Zhang
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Reference Evapotranspiration ◽  
Temperature Data ◽  
Generalized Regression Neural Network ◽  
Generalized Regression ◽  
Learning Machine

Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau

2016 ◽  
Vol 48 (4) ◽  
pp. 1156-1168 ◽  
Author(s):  
Yu Feng ◽  
Daozhi Gong ◽  
Xurong Mei ◽  
Ningbo Cui
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Crop Coefficient ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Area Index ◽  
Generalized Regression ◽  
Learning Machine

Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (hc) were continuously measured during maize growing seasons of 2011–2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (Kc) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network

2015 ◽  
Vol 793 ◽  
pp. 483-488
Author(s):  
N. Aminudin ◽  
Marayati Marsadek ◽  
N.M. Ramli ◽  
T.K.A. Rahman ◽  
N.M.M. Razali ◽  
...  
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Risk Index ◽  
Weather Condition ◽  
Generalized Regression Neural Network ◽  
Voltage Collapse ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Generalized Regression

The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

Sign in / Sign up

Export Citation Format

Share Document