New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM)

2020 ◽  
pp. 181-199
Author(s):  
Salim Heddam ◽  
Ozgur Kisi ◽  
Abderrazek Sebbar ◽  
Larbi Houichi ◽  
Lakhdar Djemili
Keyword(s):  
Extreme Learning Machine ◽  
Mediterranean Region ◽  
Reference Evapotranspiration ◽  
The Mediterranean ◽  
Learning Machine ◽  
New Formulation

scholarly journals Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration

2017 ◽  
Vol 08 (2) ◽  
pp. 127-140 ◽  
Author(s):  
Yubin Zhang ◽  
◽  
Zhengying Wei ◽  
Lei Zhang ◽  
Qinyin Lin ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Reference Evapotranspiration ◽  
Learning Machine

scholarly journals New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine

2017 ◽  
Vol 49 (3) ◽  
pp. 939-953 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi
Keyword(s):  
Extreme Learning Machine ◽  
Polynomial Regression ◽  
Evaluation Criteria ◽  
Computing Methods ◽  
Watershed Hydrology ◽  
Streamflow Forecasting ◽  
Daily Streamflow ◽  
Evolutionary Polynomial Regression ◽  
Learning Machine ◽  
New Formulation

Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.

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