scholarly journals Feasibility of soft computing techniques for estimating the long-term mean monthly wind speed

2022 ◽  
Vol 8 ◽  
pp. 638-648
Author(s):  
Shahab S. Band ◽  
Sina Ardabili ◽  
Amir Mosavi ◽  
Changhyun Jun ◽  
Helaleh Khoshkam ◽  
...  
2016 ◽  
Vol 07 (01) ◽  
pp. 1338-1343
Author(s):  
Manju Khanna ◽  
◽  
Srinath N.K. ◽  
Mendiratta K. ◽  
◽  
...  

Author(s):  
Pijush Samui ◽  
Yıldırım Dalkiliç

This chapter examines the capability of three soft computing techniques (Genetic Programming [GP], Support Vector Machine [SVM], and Multivariate Adaptive Regression Spline [MARS]) for prediction of wind speed in Nigeria. Latitude, longitude, altitude, and the month of the year have been used as inputs of GP, RVM, and MARS models. The output of GP, SVM, and MARS is wind speed. GP, SVM, and MARS have been used as regression techniques. To develop GP, MARS, and SVM, the datasets have been divided into the following two groups: 1) Training Dataset – this is required to develop GP, MPMR, and RVM models. This study uses 18 stations' data as a training dataset. 2) Testing Dataset – this is required to verify the developed GP, MPMR, and RVM models. The remaining 10 stations data have been used as testing dataset. Radial basis function has been used as kernel functions for SVM. A detailed comparative study between the developed GP, SVM, and MARS models is performed in this chapter.


2015 ◽  
Vol 81 (5-8) ◽  
pp. 771-778 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Gerardo Maximiliano Méndez ◽  
Juan Pablo Nieto González ◽  
Jesús de la Rosa Elizondo

Sign in / Sign up

Export Citation Format

Share Document