An improved genetic programming to SSM/I estimation typhoon precipitation over ocean |
| |
| Authors: | Li Chen Keh‐Chia Yeh Hsiao‐Ping Wei Gin‐Rong Liu |
| |
| Institution: | 1. Department of Civil Engineering, Chung Hua University, Hsinchu 30012, Taiwan, ROC;2. Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan, ROC;3. National Science and Technology Center for Disaster Reduction, Taipei 32001, Taiwan, ROC;4. Center for Space and Remote Sensing Research, National Central University, Tao‐Yuan 32001, Taiwan, ROC |
| |
| Abstract: | This article proposes an improved multi‐run genetic programming (GP) and applies it to estimate the typhoon rainfall over ocean using multi‐variable meteorological satellite data. GP is a well‐known evolutionary programming and data mining method used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. However, the searching efficiency of traditional GP can be decreased by the complex structure of parse tree to represent the multiple input variables. This study processed an improvement to enhance escape ability from local optimums during the optimization procedure. We continuously run GP several times by replacing the terminal nodes at the next run with the best solution at the current run. The current method improves GP, obtaining a highly nonlinear meaningful equation to estimate the rainfall. In the case study, this improved GP (IGP) described above combined with special sensor microwave imager (SSM/I) seven channels was employed. These results are then verified with the data from four offshore rainfall stations located on islands around Taiwan. The results show that the IGP generates sophisticated and accurate multi‐variable equation through two runs. The performance of IGP outperforms the traditional multiple linear regression, back‐propagated network (BPN) and three empirical equations. Because the extremely high values of precipitation rate are quite few and the number of zero values (no rain) is very large, the underestimations of heavy rainfall are obvious. A simple genetic algorithm was therefore used to search for the optimal threshold value of SSM/I channels, detecting the data of no rain. The IGP with two runs, used to construct an appropriate mathematical function to estimate the precipitation, can obtain more favourable results from estimating extremely high values. Copyright © 2011 John Wiley & Sons, Ltd. |
| |
| Keywords: | genetic programming meteorological satellite SSM/I evolutionary programming data mining back‐propagated network (BPN) |
|
|
