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| Titel |
Investigating rainfall estimation from radar measurements using neural networks |
| VerfasserIn |
A. Alqudah, V. Chandrasekar, M. Le |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 3 ; Nr. 13, no. 3 (2013-03-04), S.535-544 |
| Datensatznummer |
250018377
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| Publikation (Nr.) |
 copernicus.org/nhess-13-535-2013.pdf |
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| Zusammenfassung |
| Rainfall observed on the ground is dependent on the four dimensional
structure of precipitation aloft. Scanning radars can observe the four
dimensional structure of precipitation. Neural network is a nonparametric
method to represent the nonlinear relationship between radar measurements
and rainfall rate. The relationship is derived directly from a dataset
consisting of radar measurements and rain gauge measurements. The
performance of neural network based rainfall estimation is subject to many
factors, such as the representativeness and sufficiency of the training
dataset, the generalization capability of the network to new data, seasonal
changes, and regional changes. Improving the performance of the neural
network for real time applications is of great interest. The goal of this
paper is to investigate the performance of rainfall estimation based on
Radial Basis Function (RBF) neural networks using radar reflectivity as
input and rain gauge as the target. Data from Melbourne, Florida NEXRAD
(Next Generation Weather Radar) ground radar (KMLB) over different years
along with rain gauge measurements are used to conduct various
investigations related to this problem. A direct gauge comparison study is
done to demonstrate the improvement brought in by the neural networks and to
show the feasibility of this system. The principal components analysis (PCA)
technique is also used to reduce the dimensionality of the training dataset.
Reducing the dimensionality of the input training data will reduce the
training time as well as reduce the network complexity which will also avoid
over fitting. |
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