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Predicting rainfall intensity using a genetic algorithm approach
- Publication Year :
- 2007
-
Abstract
- A genetic algorithm rainfall intensity (GARI) model has been developed and used to predict the intensities for given return period. It is a one-step solution procedure that may not require any mathematical transformation. The problem formulation is given and the genetic algorithm solution of the problem is presented. The results show that the proposed GARI model can be used to solve the rainfall intensity-duration-frequency relations with lowest mean-squared error between measured and predicted intensities. Predicted intensities are in good agreement between measured and predicted values for given return periods. Copyright (c) 2006 John Wiley & Sons, Ltd.
- Subjects :
- Return period
Rain
Rainfall intensity-duration-frequency relations
precipitation intensity
return period
Weather forecasting
Genetic algorithm rainfall intensity (GARI) model
Statistics
Genetic algorithm
Parameter estimation
Rainfall intensity
Water Science and Technology
Mathematics
estimation method
Mathematical models
Estimation theory
rainfall intensity
genetic algorithm
parameter estimation
Mean square error
prediction
Genetic algorithms
precipitation assessment
parameterization
Transformation (function)
Mathematical transformations
Intensity (heat transfer)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....bfeee0eec258573737c4a482a0cf9f60