神经网络在气候预测和插值中的适用性:综合文献综述

S. Karmakar, Siddhartha Choubey, P. Mishra
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引用次数: 0

摘要

为了了解神经网络在气候预测和空间插值中的适用性,本文对近50年来的相关文献进行了综述。结果表明,BPN、RBF等神经网络最适合预测降雨、降雨径流等气候变量的混沌行为,具有较长的预测周期。神经网络对平均气候的空间插值具有重要意义
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review
To be familiar with appropriateness of Neural Network in climate prediction and spatial interpolation, e comprehensive literature review of past 50 years is done and offered in this paper. And it is established that Neural Network such as BPN, RBF is best appropriate to be predicted chaotic behavior of climate variables like rainfall, rainfall runoff, and have efficient enough for prediction in long period. It is also found that Neural Network is significant for spatial interpolation of mean climate
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