外文摘要：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.
外文关键词：deep learning;neural networks;Agent-based model;upscaling;farm modelling;surrogate model
作者：Shang, Linmei;Heckelei, Thomas;Wang, Jifeng;Schaefer, David;Gall, Juergen;Appel, Franziska;Storm, Hugo
作者单位：Univ Bonn;Lamarr Inst Machine Learning & Artificial Intelli;Leibniz Inst Agr Dev Transit Econ IAMO
期刊名称：JOURNAL OF AGRICULTURAL ECONOMICS
期刊影响因子：0.0
出版年份：2024
出版刊次：75（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台