Huang et al. (2025) Multi-objective collaborative optimization of water resources in Hebei irrigation areas: maximizing the benefits of the water-energy-grain nexus driven by the NSGA-III algorithm and verified by digital twins

Identification

• Journal: Frontiers in Sustainable Food Systems
• Year: 2025
• Date: 2025-12-02
• Authors: Peng Huang, Mengdi Jia, F. C. Gu
• DOI: 10.3389/fsufs.2025.1701718

Research Groups

• College of Business, City University of Hong Kong, Hong Kong, China
• School of Civil Engineering, Zhengzhou University, Zhengzhou, China
• Guangdong Zhonghao Survey, Design and Consulting Co., Ltd., Guangdong, China

Short Summary

This study developed a digital twin-enabled framework for multi-objective collaborative optimization of water resources in Hebei irrigation areas, integrating an enhanced NSGA-III algorithm and a four-dimensional nexus model to maximize benefits of the water-energy-grain nexus. The framework achieved significant water conservation, energy reduction, and grain yield increase while reversing groundwater over-extraction.

Objective

• To develop an innovative paradigm that integrates intelligent algorithms with dynamic verification, specifically an improved multi-objective optimization engine with a digital twin platform, to achieve a closed-loop decision-making process of “optimization-verification-iteration” for collaborative resource management in irrigation areas. This aims to resolve the dilemma between water conservation and energy cost by maximizing the benefits of the water-energy-grain nexus.

Study Configuration

• Spatial Scale: Typical irrigation areas in Hebei Province, China, covering 1,200 square kilometers. Scalability demonstrated in the Hexi Corridor irrigation area.
• Temporal Scale: Data collection period from 2018 to 2023. Digital twin simulation step length of 900 seconds (15 minutes) with a 24-hour assimilation window. Rolling optimization mechanism with a 72-hour cycle during the irrigation season. Monthly optimization cycles reduced to 15-minute intervals.

Methodology and Data

• Models used:
  • Enhanced NSGA-III algorithm (with dynamic reference points, hybrid constraints, directional crossover operator, simulated annealing mutation, parallel architecture, and entropy weight TOPSIS for decision-making).
  • Quadruple-objective nexus model (nonlinear economic return, dynamic integral groundwater recovery, pump station operational efficiency and equipment wear, climate resilience index for food security).
  • Variational assimilation model for multi-source data fusion.
  • Differential equation model with coupled stochasticity for climate-policy interaction.
  • Geometric mean-based robustness index.
  • PID control law for policy gradient optimization.
  • Resource situation heat map based on field theory.
  • OPC UA protocol data transmission model.
  • Time-decay verification metric.
  • K-means++ clustering for dynamic reference point generation.
• Data sources:
  • 32 meteorological monitoring stations, 48 soil moisture points, and 26 groundwater monitoring wells in Hebei (2018–2023).
  • 15-minute energy consumption data from pump stations.
  • Landsat-8 remote sensing images (30 meter resolution, 2021–2022).
  • Hebei Rural Statistical Yearbook 2022.
  • Crop water productivity data from Hebei Irrigation District (2018–2022).
  • Irrigation records from the Bureau of Agriculture and Rural Affairs.
  • Hebei Agricultural Leading Variety Directory (2022 edition).
  • 28 monitoring stations in the Hexi Corridor (2018–2022).

Main Results

• The enhanced NSGA-III algorithm achieved a hypervolume index of 0.793 (23.3% improvement over NSGA-II), converged in 168 generations (21.3 minutes, 66.8% faster), and provided a solution set coverage of 86.4% in the four-dimensional objective space.
• The optimized scheme resulted in 18.5% water conservation, 1,285 kWh per hectare energy reduction, and a 5.1% increase in grain yield.
• Groundwater levels rose by 0.07 meters in dry years, reversing the trend of over-extraction.
• The digital twin platform demonstrated high accuracy with a groundwater depth prediction error of 0.03 meters, a yield simulation error of 33 kilograms per hectare (0.48%), and an energy consumption prediction deviation of 2.47%.
• Real-time control performance showed a decision latency of 186 milliseconds, a fault recovery time of 8.7 seconds, and a data throughput of 68.5 megabytes per second, supporting simultaneous regulation of 132 gates.
• The implementation cost of 485,000 USD was offset within 6.2 years due to a 19% reduction in pumping costs.
• Sensitivity analysis revealed precipitation variability (comprehensive sensitivity 0.81) as the dominant factor, guiding climate adaptation measures. Pump station efficiency sensitivity to energy targets was -0.98, and crop water use efficiency coefficient sensitivity to food was 1.35.
• The framework demonstrated scalability in the Hexi Corridor, achieving 15.3% water savings and 77% decision acceptance under 180 millimeters of annual precipitation.

Contributions

• First-time multi-objective collaborative optimization of the water-energy-grain nexus system, successfully balancing resource competition and ecological protection.
• Development of a digital twin-enabled framework integrating three key innovations:
  1. An enhanced NSGA-III algorithm with dynamic reference points and hybrid constraints, significantly improving 4D solution coverage (by 22.3%) and convergence efficiency.
  2. A quadruple-objective nexus model that integrates economic diminishing returns, groundwater dynamics, pump energy-lifecycle coupling, and climate-resilient yield.
  3. A real-time digital twin loop implementing PID-controlled verification with a 186 millisecond decision latency, achieving the first closed-loop execution in agricultural resource management.
• Breaks through the limitations of traditional static optimization, transforming irrigation district management from experience-driven to model-driven.
• Provides a replicable technical paradigm for water-scarce areas and actionable solutions for 89% of China’s water-stressed farmlands, establishing a sustainable development path encompassing resource, ecology, and economy.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Citation

@article{Huang2025Multiobjective,
  author = {Huang, Peng and Jia, Mengdi and Gu, F. C.},
  title = {Multi-objective collaborative optimization of water resources in Hebei irrigation areas: maximizing the benefits of the water-energy-grain nexus driven by the NSGA-III algorithm and verified by digital twins},
  journal = {Frontiers in Sustainable Food Systems},
  year = {2025},
  doi = {10.3389/fsufs.2025.1701718},
  url = {https://doi.org/10.3389/fsufs.2025.1701718}
}