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

Abstract

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. Keywords: remote sensing; EOS/MODIS; artificial neural network (ANN); back propagation algorithm (BPA); genetic algorithm (GA); distributed rainfall estimation 1. INTRODUCTION Rainfall precipitation is an important but highly variable atmospheric parameter, and in a large river basin, different area has different weather condition, conventional methods of retrieved meteorological parameters are pretty difficult to satisfy the hydrological need. While the technology of remote sensing can obtain the distributed meteorological parameters in each unit area of the river basin, therefore, remote sensing is more effective and convenient than conventional methods in relevant surveys and studies. Moreover, the existing rainfall station network cannot provide the temporal and spatial coverage which are necessary for sufficient monitoring, so their application for accurate precipitation estimation with good temporal and spatial coverage is hampered by the existing technical limitation problems. Compared with the existing rainfall station network, the satellite measurements have the advantage of providing spatially and temporally homogeneous observations over a large area, such as GMS, TM, AVHRR and MODIS satellite images. In these satellite sensors, the moderate resolution imaging spectroradiometer (MODIS) has the wide spectral range and spatial coverage of 36 spectral bands sampling the electromagnetic spectrum from 0.4 to 14 um with a spatial resolution ranging from 250 to 1,000 meters