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.
Highlights
Damaged by floods are natural disasters that have violence cause significant damage and economic and social
They can conclude that the fuzzy set model considering 4 variables using rainfall duration data is more effective than the model using the continuous rainfall data
The model was applied to estimate the rainfall in the Chi River Basin using 5-minute historic data
Summary
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. An estimated rainfall data is important information for prevention disasters. Measures to prevent and mitigate damage caused by flooding to effectively and promptly. Rainfall forecast was studied by using mathematical models (Frencha et al, 1992; Hormwichian et al, 2009; Otok and Suhartono, 2009; Andrieu et al, 1996). These models required a lot of hydrological and meteorological data.
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