Article: Forecasting trend of agricultural talents flow by spatio-temporal graph neural network and LightGBM Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.8 pp.1 - 18 Abstract: Current agricultural talent flow prediction mainly uses single models (e.g., linear regression, ARIMA, LSTM), which fail to capture non-Euclidean spatial-temporal relationships and automatically extract complex spatio-temporal interactions, limiting accuracy and interpretability. This paper proposes a hybrid framework integrating a spatio-temporal graph neural network (STGNN) and LightGBM. Using 2010-2020 data from 17 cities in Henan Province, a spatio-temporal graph is built with city nodes and geographic-threshold edges. STGNN combines graph convolution and temporal convolution (TCN) to automatically learn spatio-temporal features, while LightGBM regresses lag and socio-economic indicators for interpretability. Benchmark comparisons with ARIMA, LSTM, and LightGBM, plus ablation and sensitivity tests, confirm the hybrid model's superiority. It reduces error by 10%-14% versus standalone STGNN/LightGBM, achieving under 12.3% overall error, with significantly improved accuracy and stability. Inderscience Publishers - linking academia, business and industry through research

Title: Forecasting trend of agricultural talents flow by spatio-temporal graph neural network and LightGBM

Authors: Jie Liu

Addresses: School of Management, Xinxiang University, Xinxiang, 453003, China

Abstract: Current agricultural talent flow prediction mainly uses single models (e.g., linear regression, ARIMA, LSTM), which fail to capture non-Euclidean spatial-temporal relationships and automatically extract complex spatio-temporal interactions, limiting accuracy and interpretability. This paper proposes a hybrid framework integrating a spatio-temporal graph neural network (STGNN) and LightGBM. Using 2010-2020 data from 17 cities in Henan Province, a spatio-temporal graph is built with city nodes and geographic-threshold edges. STGNN combines graph convolution and temporal convolution (TCN) to automatically learn spatio-temporal features, while LightGBM regresses lag and socio-economic indicators for interpretability. Benchmark comparisons with ARIMA, LSTM, and LightGBM, plus ablation and sensitivity tests, confirm the hybrid model's superiority. It reduces error by 10%-14% versus standalone STGNN/LightGBM, achieving under 12.3% overall error, with significantly improved accuracy and stability.

Keywords: agricultural talent flow; spatio-temporal graph neural network; STGNN; LightGBM; hybrid prediction.

DOI: 10.1504/IJICT.2026.151601

International Journal of Information and Communication Technology, 2026 Vol.27 No.8, pp.1 - 18

Received: 29 Aug 2025
Accepted: 18 Sep 2025
Published online: 09 Feb 2026 *

Title: Forecasting trend of agricultural talents flow by spatio-temporal graph neural network and LightGBM

Keep up-to-date