TY - JOUR

T1 - UReslham

T2 - Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations

AU - Jin, Zhengyong

AU - Xu, Xiaolong

AU - Bilal, Muhammad

AU - Wu, Songyu

AU - Lin, Huichao

PY - 2024/6/30

Y1 - 2024/6/30

N2 - The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

AB - The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

KW - deep learning

KW - smart agriculture

KW - spatial federated learning

KW - remote sensing

KW - radar reflectivity

U2 - 10.1111/coin.12684

DO - 10.1111/coin.12684

M3 - Journal article

VL - 40

JO - Computational Intelligence

JF - Computational Intelligence

IS - 3

M1 - e12684

ER -