An Estimation of Rainfall using Fuzzy Set-Genetic Algorithms Model

Abstract

Problem statement: Damaged by floods are natural disasters that have violence cause significant damage and economic and social. If we can prevent disasters that may occur in advance is important. So an estimated rainfall data is important information for prevention disasters. Approach: The objective of this study is to apply a fuzzy set theory to estimate rainfall. The genetic algorithm was applied to calibrate the fuzzy set model. The proposed model considered only a few basic hydrological parameters including temperature, humidity, wind speed and solar radiation. The proposed model was applied to estimate the rainfall in the Chi River Basin (in the northeast region of Thailand) using 5- minute historic data. Results: The results have shown that the obtained rainfalls of the improved model are close to the rainfall of the actual rainfall record. Furthermore, the results presented that the genetic algorithm calibration provided the optimal condition of membership function. Conclusions/Recommendations: The proposed fuzzy-GA model can be used to estimate the rainfall, given only the basic hydrological parameters; temperature, humidity, wind speed and solar radiation. The fuzzy set model considering 4 variables using rainfall duration data is more effective than the model using the continuous rainfall data.