Surrogate modelling of a detailed farm‐level model using deep learning
利用深度学习技术构建农场模型FarmDyn的替代模型，以解决其与基于主体的模型集成时的计算和调试难题，并评估不同神经网络架构在拟合度、推理时间和数据需求间的权衡。
Abstract Technological change co‐determines agri‐environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm‐level models that are rich in technology details and environmental indicators, integrated with agent‐based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade‐offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi‐directional Long Short Term Memory.