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- W2319586548 abstract "In this paper, an investigation on applying dynamic Neural Networks in long-lead rainfall forecasting is presented. Among different components in building ANNs, time delay operators, recurrent connections and the hybrid method are used to design the temporal neural networks. Using these components, three types of temporal network architectures are investigated. Recurrent neural network, time delay neural network, and time delay recurrent neural network are used to forecast monthly precipitation time series from one to six months lead time. These models are applied to forecast the rainfall of Karoon drainage basin in south-western part of Iran based on 31 years of monthly data. 21 years of data are used for models' calibration and 10 remained years are used for models' validation. Besides, a conventional multilayer perceptron network and an autoregressive integrated moving average model are investigated in order to compare the temporal networks performance. The validation results show that all temporal neural networks especially time delay recurrent neural network perform significantly better than standard MLP and ARIMA models in long-lead rainfall forecasting." @default.
- W2319586548 created "2016-06-24" @default.
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- W2319586548 date "2005-07-01" @default.
- W2319586548 modified "2023-09-27" @default.
- W2319586548 title "Application of Temporal Neural Networks in Long-Lead Rainfall Forecasting" @default.
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- W2319586548 doi "https://doi.org/10.1061/40792(173)266" @default.
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