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Focused Time Delay Neural Network Model for Rainfall Prediction Using Indian Ocean Dipole Index

机译:采用印度洋偶极指数的降雨预测聚焦时滞神经网络模型

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Indian monsoon rainfall is a process which is dependent on number of ecological and geographical parameter. This makes it very difficult to accurately predict the monsoon rainfall. As India is agriculture base country a long range monsoon rainfall prediction is crucial for proper planning and management of agriculture strategy. Many researcher have identified influence of the Indian Ocean Dipole (IOD) on the inter annual variability of the Indian summer monsoon rainfall (ISMR). The Indian Ocean Dipole (IOD) is a coupled ocean-atmosphere phenomenon in the Indian Ocean. It is normally characterized by anomalous cooling of SST in the south eastern equatorial Indian Ocean and anomalous warming of SST in the western equatorial Indian Ocean. In this paper a Time lag neural network model with gamma memory processing neuron is proposed for one month ahead prediction of monsoon rainfall in the country based upon the Indian Ocean dipole parameter.
机译:印度季风降雨是一种依赖生态和地理参数数量的过程。 这使得能够准确预测季风降雨非常困难。 由于印度是农业基础国家,长距离的季风降雨预测对于农业战略的适当规划和管理至关重要。 许多研究员已经确定了印度洋偶极子(IOD)对印度夏季季风降雨(ISMR)的年度变异性的影响。 印度洋偶极(IOD)是印度洋的耦合海洋气氛现象。 它通常是在西部赤道印度洋中SST的异常冷却,在西部赤道印度洋中SST的异常变暖。 本文提出了一种基于印度洋偶极参数的全国季风降雨的一个月预测,提出了一种与伽马记忆处理神经元的时间滞后神经网络模型。

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