Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: A case study on the Shennongjia area, Central China

2019 ◽  
Vol 181 ◽  
pp. 106200 ◽  
Author(s):  
Chao Gan ◽  
Wei-Hua Cao ◽  
Min Wu ◽  
Xin Chen ◽  
Yu-Le Hu ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Central China ◽  
Support Vector ◽  
Drilling Rate ◽  
Rate Of Penetration

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

Modelling algal abnormal proliferation in a reservoir using support vector regression: a case study

Ecohydrology ◽  
2014 ◽  
Vol 8 (6) ◽  
pp. 1109-1118 ◽  
Author(s):  
C. Díaz Muñiz ◽  
J. R. Alonso Fernández ◽  
P. J. García Nieto ◽  
J. C. Alvarez Antón
Keyword(s):  
Support Vector Regression ◽  
Support Vector

A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY

2010 ◽  
Vol 09 (02) ◽  
pp. 87-104 ◽  
Author(s):  
JIANSHENG WU ◽  
MINGZHE LIU ◽  
LONG JIN
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Support Vector Regression ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Support Vector Regression Model ◽  
Swarm Optimization

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application.

scholarly journals Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0179763 ◽  
Author(s):  
Bing-Chun Liu ◽  
Arihant Binaykia ◽  
Pei-Chann Chang ◽  
Manoj Kumar Tiwari ◽  
Cheng-Chin Tsao
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Urban Air Quality ◽  
Support Vector ◽  
Urban Air ◽  
Collaborative Support ◽  
Air Quality Forecasting
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