TY - JOUR
T1 - Learning networks for tornado detection
AU - Trafalis, Theodore B.
AU - Santosa, Budi
AU - Richman, Michael B.
N1 - Funding Information:
The present work has been partially supported by the NSF grant EIA-0205628.
PY - 2006/2
Y1 - 2006/2
N2 - In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and minimax probability machines (MPMs) are applied for tornado detection. The last two approaches utilize kernel methods to address non-linearity of the data in the input space. All methods are applied to detect when tornadoes occur, using variables based on radar derived velocity data and month number. Computational results indicate that BNNs are more accurate for tornado detection over a suite of forecast evaluation indices.
AB - In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and minimax probability machines (MPMs) are applied for tornado detection. The last two approaches utilize kernel methods to address non-linearity of the data in the input space. All methods are applied to detect when tornadoes occur, using variables based on radar derived velocity data and month number. Computational results indicate that BNNs are more accurate for tornado detection over a suite of forecast evaluation indices.
KW - Artificial neural networks
KW - Bayesian neural networks
KW - Detection
KW - Kernel functions
KW - Minimax probability machines
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=34047121528&partnerID=8YFLogxK
U2 - 10.1080/03081070500502850
DO - 10.1080/03081070500502850
M3 - Article
AN - SCOPUS:34047121528
SN - 0308-1079
VL - 35
SP - 93
EP - 107
JO - International Journal of General Systems
JF - International Journal of General Systems
IS - 1
ER -