Application of EOS/MODIS remote sensing dataset to ANN/GA modeling of distributed precipitation estimation

Guangyi Hu; Qiuwen Zhang; Wenbo Li

doi:10.1117/12.750241

14 November 2007 Application of EOS/MODIS remote sensing dataset to ANN/GA modeling of distributed precipitation estimation

Guangyi Hu, Qiuwen Zhang, Wenbo Li

Author Affiliations +

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 679024 (2007) https://doi.org/10.1117/12.750241
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The main meteorological parameters which influencing the rainfall can be distilled from the MODIS satellite cloud imagery and the artificial neural network (ANN) model constructed by these meteorological parameters and can be applied on distributed rainfall estimation. Because it is difficult to decide the structure of back propagation neural network (BPNN) and to solve the problem of local convergence, an appropriate training and modeling method of ANN such as the real code genetic algorithm (RGA) is vital to the accuracy of rainfall estimation. The data of the simulation tests show that the Mean Relative Error (MRE) of BPA model is 23.6%, while the MRE of RGA model is 20.7%, Compared with the ANN trained by BPA, the estimation error of the ANN trained by RGA is cut down by 2.9%, and the Root Mean Squared Error (RMSE) is cut down by 2.5% at the same time, hence, the results prove that the ANN model trained using RGA will significantly outperform the back propagation algorithm (BPA) trained ANN model and improve the precision of rainfall estimation.

Citation Download Citation

Guangyi Hu, Qiuwen Zhang, and Wenbo Li "Application of EOS/MODIS remote sensing dataset to ANN/GA modeling of distributed precipitation estimation", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679024 (14 November 2007); https://doi.org/10.1117/12.750241

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