In this regard, this paper selects Poyang Lake since the exploration item, adopts the transformer neural network method to establish the prediction design of everyday scale of water amount of Poyang Lake, and explores its likely to forecast the water degree modify process of Poyang Lake. We Look at the simulation effects of transformer design with other deep Finding out networks for instance LSTM, and distinguish the influences of inflows with the “five river” and Yangtze River discharges on the lake water versions. We present an analysis the consequences of hyper-parameters and forecasting guide time on model accuracy. The contribution points of the examine are as follows: (1) Here is the very first review to apply deep Studying model coupling With all the self-focus mechanism on lake water amount prediction.
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Later on, the consequences from Yangtze River plus the TGD must be regarded as one among A very powerful things within the water degree forecasting of Poyang Lake.
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The position info has the identical dimension 512 as the input embeddings, so the two is often additional instantly. The situation embedding is represented as follows:
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