SS-ConvSLSTM: A Study on a Spatiotemporal Temperature Prediction Model for Henan Based on Improved sLSTM

Zhongcai Li

doi:10.54097/wn7ah797

Authors

Zhongcai Li

DOI:

https://doi.org/10.54097/wn7ah797

Keywords:

Temperature prediction, MAU, Sequence-to-sequence prediction, LSTM, ConvLSTM

Abstract

As a major grain-producing province in China, accurate temperature prediction is crucial for Henan. Addressing the issue of temperature prediction in Henan, this paper proposes a spatiotemporal sequence prediction model named SS-ConvSLSTM, based on an improved sLSTM. The model achieves this through three core innovations: firstly, it employs a 48×48 spatiotemporal data grid for regional holistic prediction, as opposed to traditional single-point forecasting; secondly, it modifies the sLSTM by introducing a convolutional structure to construct ConvSLSTM units, making it suitable for spatiotemporal prediction tasks; and finally, an encoder-decoder network architecture, SS-ConvSLSTM, is built based on the new units to accomplish sequence-to-sequence prediction tasks. Experimental results demonstrate that the model performs excellently in temperature prediction tasks for Henan, achieving significantly higher prediction accuracy compared to traditional baseline models such as LSTM and ConvLSTM, thereby providing a new technological pathway for regional climate prediction.

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