Fuzzy-GA Approach for Estimating Rainfall over Upper Chi-Mun Basins of Thailand

Authors
Department of Civil Engineering, Faculty of Engineering, Mahasarakham University, 44150, Thailand.
Abstract
The present study examines the fuzzy sets model for computing rainfall over the Upper Chi-Mun basins in the Northeastern region of Thailand based on historical weather data from five stations’ rain gauges under the radar umbrella, temperature, relative humidity, and radar reflectivity. Data were collected during June 2009 to August 2009 of the rainfall reflectivity record from the Royal Rainmaking Research Centre at Pimai, Nakhon Ratchasima Province, and for the surface rainfall, automatic rain gauges were used. The results showed that the Fuzzy-GAs model could be used effectively to estimate rainfall given only three parameters: temperature, relative humidity and radar reflectivity. Furthermore, the results show that the genetic algorithm calibration provided the optimal conditions of the membership function. The simulation results indicated that the results of the Fuzzy-GA model were close to the observed rainfall data more than the results of a multiple linear regression model for both calibration and validation processes. Consequently, we are confident that a Fuzzy-GA model is a useful tool for estimating rainfall.

Keywords


1. Compliew, S. and Khuanyuen, B. 2003. The Relation of Radar Reflectivity and Ground Rainfall in the Northeast of Thailand. Agricultural Engineering 4th Proceeding, KU Home Kasetsart University, Bangkok, Thailand.
2. Franchini, M. 1996. Using a Genetic Algorithm Combined with a Local Search Method for the Automatic Calibration of Conceptual Rainfall-Runoff Models. Hydrolog. Sci. J., 41(1): 21-40.
3. Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company Inc., London.
4. Holland, J. H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.
5. Jairaj, P. G. and Vedula, S. 2000. Multireservoir System Optimization Using Fuzzy Mathematical Programming. Water Resour. Manag., 14(6): 457-472.
6. Jang, S. R., Sun, C. T. and Mizutani, E. 1997. Neuro-Fuzzy and Soft Computing. Prentice-Hall Inc., USA.
7. Kangrang, A. and Chaleeraktrakoon, C. 2007. Genetic Algorithms Connected Simulation with Smoothing Function for Searching Rule Curves. Am. J. Appl. Sci., 4(2): 73-79.
8. Piman, T., Babel, M. S., Gupta, A. D. and Weesakul, S. 2007. Development of a Window Correlation Matching Method for Improved Radar Rainfall Estimation. Hydrol. Earth Syst. Sc., 11: 1361-1372.
9. Saruwatari, N. and Yomota, A. 1995. Forecasting System of Irrigation Water on Paddy Field by Fuzzy Theory. Agric. Water Manage., 28: 163-167.
10. Tantanee, S., Prakarnrat, S., Polsan, P. and Weesakul, U. 2008. Estimation of Rainfall-Radar Reflectivity Relationship Using Buffer Probability Technique (BPT). Proceeding 4th IASME/WSEAS International Conference on Energy, Environmental, Ecosystems and Sustainable Development (EEESD’08), PP. 460-466.
11. Thongwan, T., Kangrang, A. and Homwuttiwong, S. 2011. An Estimation of Rainfall Using Fuzzy Set-genetic Algorithms Models. Am. J. Engg. Appl. Sci., 4(1): 77-81.
12. Wang, Q. J. 1991. The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-runoff Models. Water Resour. Res., 27(9): 2467-2471.
13. Zadeh, L. A. 1965. Fuzzy Sets. Information Control., 8: 338-353.
14. Zadeh, L. A. 1998. Roles of Soft Computing and Fuzzy Logic in Conception Design and Deployment of Information/Intelligent Systems. Adv. Study Inst. Soft Comput. Appl. Antalya, 162: 1-9.