Article: Water-economic collaborative management in the Yellow River Basin by dual-channel adaptive spatio-temporal graph transformer Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.14 pp.22 - 34 Abstract: The Yellow River Basin faces severe water resource constraints, posing dual challenges of ecological protection and high-quality development. Traditional predictive models cannot simultaneously capture the topological constraints of natural hydrological processes and the interaction patterns of socio-economic trade networks. To address this, this study proposes a dual-channel adaptive spatio-temporal graph transformer (DA-STGT) model. The model integrates multi-source hydrological and economic data to construct a high-precision spatio-temporal dataset for the basin. It employs a parallel graph convolution structure for the 'physical water network' and the 'economic gravity network', effectively decoupling unidirectional water flows from bidirectional economic influences. Experimental results show that DA-STGT performs exceptionally in predicting basin-wide water supply and demand, reducing root mean square error (RMSE) to 3.31 (108 m3). Statistical tests (p < 0.01) confirm its superiority over existing benchmarks. Scenario simulations further indicate that coordinated scheduling under extreme drought conditions can reduce total economic losses across the basin by 23.1%. This study establishes a robust, data-driven decision-making framework, offering quantitative support for sustainable basin management and providing methodological guidance for interdisciplinary fields such as socio-hydrology and water resource economics. Inderscience Publishers - linking academia, business and industry through research

Title: Water-economic collaborative management in the Yellow River Basin by dual-channel adaptive spatio-temporal graph transformer

Authors: Kang Gao

Addresses: School of Foreign Studies, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China

Abstract: The Yellow River Basin faces severe water resource constraints, posing dual challenges of ecological protection and high-quality development. Traditional predictive models cannot simultaneously capture the topological constraints of natural hydrological processes and the interaction patterns of socio-economic trade networks. To address this, this study proposes a dual-channel adaptive spatio-temporal graph transformer (DA-STGT) model. The model integrates multi-source hydrological and economic data to construct a high-precision spatio-temporal dataset for the basin. It employs a parallel graph convolution structure for the 'physical water network' and the 'economic gravity network', effectively decoupling unidirectional water flows from bidirectional economic influences. Experimental results show that DA-STGT performs exceptionally in predicting basin-wide water supply and demand, reducing root mean square error (RMSE) to 3.31 (10⁸ m³). Statistical tests (p < 0.01) confirm its superiority over existing benchmarks. Scenario simulations further indicate that coordinated scheduling under extreme drought conditions can reduce total economic losses across the basin by 23.1%. This study establishes a robust, data-driven decision-making framework, offering quantitative support for sustainable basin management and providing methodological guidance for interdisciplinary fields such as socio-hydrology and water resource economics.

Keywords: Yellow River Basin; synergy between water resources and economy; dual-channel adaptive spatio-temporal graph transformer; DA-STGT; multi-dimensional feature fusion; spatio-temporal prediction.

DOI: 10.1504/IJRIS.2026.154238

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.14, pp.22 - 34

Received: 09 Feb 2026
Accepted: 25 Mar 2026
Published online: 17 Jun 2026 *

Title: Water-economic collaborative management in the Yellow River Basin by dual-channel adaptive spatio-temporal graph transformer

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