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Title	Lightweighting The Prediction Process Of Urban States With Parameter Sharing And Dilated Operations
ID_Doc	35286
Authors	Wang P.; Yang H.; Zhang H.; Cheng S.; Lu F.; Chen Z.
Year	2025
Published	International Journal of Digital Earth, 18, 1
DOI	http://dx.doi.org/10.1080/17538947.2025.2468414
Abstract	Lightweight and high-precision prediction models for urban states are anticipated to run efficiently on resource-limited devices, serving as key technologies for realizing smart city management. However, many existing models, despite achieving high prediction precision, suffer from overly complex designs, leading to low computational efficiency, a large number of learnable parameters, and difficulty in hyper-parameter calibration. In this study, we present a lightweight parameter-shared dilated convolutional network (PSDCN) to address these challenges. Specifically, we define parameter-shared temporal/graph dilated convolution operators to efficiently and accurately capture spatio-temporal correlations without significantly increasing model's computation time and scale of learnable parameters. Furthermore, we establish mathematical relationships between hyperparameters, significantly reducing their number and simplifying the calibration process. The PSDCN model was validated using PM2.5, traffic, and temperature datasets. The results demonstrated that the PSDCN model simplifies hyperparameter calibration. It also either outperforms or matches the prediction accuracy of nine baselines, while achieving better time efficiency and requiring fewer learnable parameters. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Author Keywords	dilated operation; hyper-parameter dependence; parameter sharing; spatio-temporal prediction; Urban states

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