ReLU Surrogates in Mixed-Integer MPC for Irrigation Scheduling
<|reference_start|>ReLU Surrogates in Mixed-Integer MPC for Irrigation Scheduling: Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problems in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing ReLU surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the widely used triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times -- by up to 99.5\% -- while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach maintains enhanced performance in terms of total prescribed irrigation and IWUE compared to the widely-used triggered irrigation scheduling method.<|reference_end|>
@article{agyeman2024relu,
    title={ReLU Surrogates in Mixed-Integer MPC for Irrigation Scheduling},
    author={Bernard T. Agyeman and Jinfeng Liu (University of Alberta) and Sirish
  L. Shah},
    journal={arXiv preprint arXiv:2409.12082},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12082},
    primaryClass={eess.SY cs.SY math.OC}
}