Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2011, Special Issue (2011), Article ID 246286, 14 pages.

Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

Nam Do Hoai, Keiko Udo, and Akira Mano

Full-text: Open access

Abstract

Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP) neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model) for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.

Article information

Source
J. Appl. Math., Volume 2011, Special Issue (2011), Article ID 246286, 14 pages.

Dates
First available in Project Euclid: 12 August 2011

Permanent link to this document
https://projecteuclid.org/euclid.jam/1313170642

Digital Object Identifier
doi:10.1155/2011/246286

Zentralblatt MATH identifier
1213.86001

Citation

Do Hoai, Nam; Udo, Keiko; Mano, Akira. Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction. J. Appl. Math. 2011, Special Issue (2011), Article ID 246286, 14 pages. doi:10.1155/2011/246286. https://projecteuclid.org/euclid.jam/1313170642


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