Rainfall-runoff forecasting using optimizing support vector regression based on hybrid genetic algorithm and simulated annealing

2014 ◽  
Author(s):  
Xiaoming Pan ◽  
Jiansheng Wu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Support Vector Regression ◽  
Hybrid Genetic Algorithm ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Runoff Forecasting

scholarly journals Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3049
Author(s):  
Khaled A. Alawi Al-Sodani ◽  
Adeshina Adewale Adewumi ◽  
Mohd Azreen Mohd Ariffin ◽  
Mohammed Maslehuddin ◽  
Mohammad Ismail ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Compressive Strength ◽  
Support Vector Regression ◽  
Model Development ◽  
Hybrid Genetic Algorithm ◽  
Limestone Powder ◽  
Support Vector ◽  
Natural Pozzolan ◽  
The One ◽  
Alkali Activated

This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.

Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm

2013 ◽  
Vol 45 (1) ◽  
pp. 111-133 ◽  
Author(s):  
Guillermo Santamaría-Bonfil ◽  
Juan Frausto-Solís ◽  
Ignacio Vázquez-Rodarte
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Hybrid Genetic Algorithm ◽  
Support Vector ◽  
Volatility Forecasting
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