Atmospheric Temperature Prediction using Support Vector Machines

By Y. Radhika, Mogalla Shashi. 2009

362 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/72d905475e7b822e90b02b96b89db42259615812

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Babak Mohammadi
2. Eric Horvitz
3. Pouya Aghelpour
4. Aditya Grover
5. Maryam Shafaei
6. Ashish Kapoor
7. Ozgur Kisi
8. Seyed Mostafa Biazar

In Environmental Engineering

1. Babak Mohammadi
2. Ozgur Kisi
3. Eric Horvitz
4. Pouya Aghelpour
5. Aditya Grover
6. Maryam Shafaei
7. Ashish Kapoor
8. Seyed Mostafa Biazar

In Management Science and Operations Research

1. Babak Mohammadi
2. Eric Horvitz
3. Pouya Aghelpour
4. Aditya Grover
5. Ashish Kapoor
6. Ozgur Kisi
7. Seyed Mostafa Biazar

Citations in the corpus

• Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA (2019, methodology): “In this study, the forecasts were analyzed in wide ranges from 2 to 10 days and the results indicated that the SVR model is a good and effective method and has better prediction accuracy when compared with MLP model (Radhika and Shashi 2009).”
• A Deep Hybrid Model for Weather Forecasting (2015, methodology): “Most work in weather forecasting to date rely on the use of generative approaches, where the weather systems are simulated via numerical methods [18, 12, 10], or rely on time-series analysis such as ARIMA models and simple classifiers based on Artificial Neural Networks [11, 10, 8, 2, 21] or Support”
• Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models (2015, background): “Radhika and Shashi (2009) modeled weather daily maximum temperatures.”