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Data mining techniques for improved WSR-88D rainfall estimation

Trafalis, T.B ; Richman, M.B ; White, A ; Santosa, B

Computers & industrial engineering, 2002-09, Vol.43 (4), p.775-786 [Periódico revisado por pares]

Seoul: Elsevier Ltd

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  • Título:
    Data mining techniques for improved WSR-88D rainfall estimation
  • Autor: Trafalis, T.B ; Richman, M.B ; White, A ; Santosa, B
  • Assuntos: Analysis ; Backpropagation ; Cluster analysis ; Clustering ; Computer architecture ; Data mining ; Data mining applications ; Dimensionality reduction ; Earth, ocean, space ; Estimating techniques ; Exact sciences and technology ; Exploratory data analysis ; External geophysics ; Feedforward neural networks ; Mean-square error ; Neural network architectures ; Neural networks ; Pattern recognition ; Principal component analysis ; Rain ; Rainfall estimation ; Remote sensing, photointerpretation ; Studies
  • É parte de: Computers & industrial engineering, 2002-09, Vol.43 (4), p.775-786
  • Descrição: The main objective of this paper is to utilize data mining and an intelligent system, Artificial Neural Networks (ANNs), to facilitate rainfall estimation. Ground truth rainfall data are necessary to apply intelligent systems techniques. A unique source of such data is the Oklahoma Mesonet. Recently, with the advent of a national network of advanced radars (i.e. WSR-88D), massive archived data sets have been created generating terabytes of data. Data mining can draw attention to meaningful structures in the archives of such radar data, particularly if guided by knowledge of how the atmosphere operates in rain producing systems. The WSR-88D records digital database contains three native variables: velocity, reflectivity, and spectrum width. However, current rainfall detection algorithms make use of only the reflectivity variable, leaving the other two to be exploited. The primary focus of the research is to capitalize on these additional radar variables at multiple elevation angles and multiple bins in the horizontal for precipitation prediction. Linear regression models and feedforward ANNs are used for precipitation prediction. Rainfall totals from the Oklahoma Mesonet are utilized for the training and verification data. Results for the linear modeling suggest that, taken separately, reflectivity and spectrum width models are highly significant. However, when the two are combined in one linear model, they are not significantly more accurate than reflectivity alone. All linear models are prone to underprediction when heavy rainfall occurred. The ANN results of reflectivity and spectrum width inputs show that a 250-5-1 architecture is least prone to underprediction of heavy rainfall amounts. When a three-part ANN was applied to reflectivity based on light, moderate to heavy rainfall, in addition to spectrum width, it estimated rainfall amounts most accurately of all methods examined.
  • Editor: Seoul: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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