Prediction of winter rainfall using Adaptive Fuzzy Neural Networks, Case study: Khorasan Razavi Province, Iran

机译：自适应模糊神经网络预测冬季降雨，案例研究：伊朗khorasanrazavi省

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This study purpose is to predict winter rainfall of Khorasan Razavi Province using Adaptive fuzzy neural networks. To do this, first average regional rainfall was calculated by Kriging method. In the next stage, rainfall correlation was analyzed by climatic predictors in different intervals. After identifying the effective predictors on regional rainfall, adaptive fuzzy neural network model was trained in 1970-1997 period and finally the rainfall was predicted for 1998-2007 period. The results show that adaptive fuzzy neural networks are able to predict the rainfall amount in an acceptable accuracy. Root mean square error was obtained 7.4 mm for the model.

机译：这项研究目的是预测利用自适应模糊神经网络预测Khorasan Razavi省的冬季降雨。为此，通过Kriging方法计算第一个平均区域降雨。在下一阶段，通过不同间隔的气候预测因子分析了降雨相关性。在确定区域降雨上有效预测因子后，自适应模糊神经网络模型于1970 - 1997年培训，最后预测了1998 - 2007年的降雨。结果表明，自适应模糊神经网络能够以可接受的准确度预测降雨量。为模型获得了7.4 mm的根均方误差。

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《International Conference on Environmental and Geological Science and Engineering》|2015年||共16页
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Gholamabbas Fallah Ghalhari; Fahimeh Shakeri;
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中图分类 X-53;
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Root mean square error; Adaptive fuzzy neural network; Climate prediction; Kriging method;

机译：根均方误差;自适应模糊神经网络;气候预测;Kriging方法;

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Prediction of winter rainfall using Adaptive Fuzzy Neural Networks, Case study: Khorasan Razavi Province, Iran

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