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Genetic Programming for Downscaling Extreme Rainfall Events

机译:减少极端降雨事件的遗传编程

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摘要

Downscaling extreme rainfall events is a major challenge in climate change study. A Genetic Programming (GP) based method is used in this article for the downscaling of extreme rainfall events in the East coast of peninsular Malaysia during northeast monsoon season. The principal components of Global Circulation Model (GCM) parameters at four points surrounding the study area are used as predictors. Four GP models are developed for the prediction of rainy days and extreme rainfall events such as rainfall more than 99 percentile, rainfall more than 95 percentile and rainfall more than 90 percentile in a year. All possible numerical, logical and trigonometric operators are used to find multi-level GP models for the downscaling. Daily rainfall data during monsoon season for the time periods 1961-1990 and 1991-2000 are used for model calibration and validation, respectively. The results show that the models can predict extreme rainfall events in the East coast of Malaysia with reasonable accuracy.
机译:减少极端降雨事件的规模是气候变化研究的主要挑战。本文使用一种基于遗传编程(GP)的方法来降低东北季风季节期间马来西亚半岛东海岸极端降雨事件的规模。研究区域周围四个点的全球循环模型(GCM)参数的主要成分用作预测变量。开发了四个GP模型来预测雨天和极端降雨事件,例如一年中的降雨量超过99%,降雨量超过95%,降雨量超过90%。所有可能的数值,逻辑和三角运算符都用于查找用于缩小的多级GP模型。分别使用1961-1990年和1991-2000年季风季节期间的每日降雨量数据进行模型校准和验证。结果表明,该模型可以合理合理地预测马来西亚东海岸的极端降雨事件。

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