Downscaling global weather forecast outputs using ANN for flood prediction

Nam Do Hoai, Keiko Udo, Akira Mano

研究成果: Article査読

26 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号246286
ジャーナルJournal of Applied Mathematics
2011
DOI
出版ステータスPublished - 2011

ASJC Scopus subject areas

  • Applied Mathematics

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