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

机译:聚焦时延神经网络模型的印度洋偶极子指数预报

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摘要

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:印度季风降雨是一个依赖于生态和地理参数数量的过程。这使得很难准确预测季风降雨。由于印度是农业大国,因此长期的季风降雨预报对于正确规划和管理农业战略至关重要。许多研究人员已经确定了印度洋偶极子(IOD)对印度夏季风降水(ISMR)年际变化的影响。印度洋偶极子(IOD)是印度洋中一种耦合的海洋-大气现象。其特征通常是赤道东南印度洋SST异常冷却和赤道印度洋西部SST异常变暖。本文基于印度洋偶极子参数,提出了一种带有伽马记忆处理神经元的时滞神经网络模型,用于对该国季风降水提前一个月的预测。

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