Abstract
Thunderstorm is a kind of severe weather with strong sudden and destructive ability. It is still difficult to warn and forecast accurately in the meteorological industry. In this paper, a neural network model of encoder decoding structure -- PRDsNET is constructed, which includes a LSTM variant structure Causal LSTM unit, high-speed characteristic channel GHU (Gradient Highway Units) and DenseBlock module in dense connection. 11 SA/SB Doppler radars and lightning data from 2017 to 2020 in Hunan province were used to verify the effect of thunderstorm recognition. In addition, multiple groups of network architectures with different encoding and decoding and multiple loss functions and optimizers were selected for cross-comparison experiments. Experimental results show that the model has an average hit rate of 95% and an average false alarm rate of 5%. The results are satisfactory and have broad application scenarios in the future meteorological automation work.
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Wang, S., Hu, D., Zhou, C., Xu, J. (2022). Thunderstorm Recognition Based on Neural Network PRDsNET Models. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_42
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DOI: https://doi.org/10.1007/978-3-030-97774-0_42
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