DocumentCode
1748781
Title
Rainfall estimation using M-PHONN model
Author
Hui Qi ; Ming Zhang
Author_Institution
Univ. of Western Sydney, Campbelltown, NSW
Volume
3
fYear
2001
fDate
2001
Firstpage
1620
Abstract
Multi-polynomial high order neural network (M-PHONN) model has been developed in this paper. The M-PHONN model for estimating heavy convective rainfall from satellite data was tested. The M-PHONN model has 5% to 15% more accuracy than the polynomial and trigonometric polynomial model and the polynomial higher order neural network models. Using ANSER-plus expert system, the average rainfall estimate errors for the total precipitation event can be reduced to less than 20%
Keywords
geophysics computing; neural nets; rain; weather forecasting; ANSER-plus expert system; M-PHONN model; multiple-polynomial high order neural network; rainfall estimation; rainfall prediction; Artificial intelligence; Artificial neural networks; Australia; Neural networks; Polynomials; Power system modeling; Satellites; Tropical cyclones; USA Councils; Weather forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938403
Filename
938403
Link To Document