Surrogate modelling of a detailed farm‐level model using deep learning

Author:

Shang Linmei1ORCID,Wang Jifeng2,Schäfer David1,Heckelei Thomas1,Gall Juergen23,Appel Franziska4ORCID,Storm Hugo1

Affiliation:

1. Institute for Food and Resource Economics (ILR) University of Bonn Bonn Germany

2. Department of Information Systems and Artificial Intelligence University of Bonn Bonn Germany

3. Lamarr Institute for Machine Learning and Artificial Intelligence Sankt Augustin Germany

4. Leibniz Institute of Agricultural Development in Transition Economies (IAMO) Halle (Saale) Germany

Abstract

AbstractTechnological 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.

Funder

Deutsche Forschungsgemeinschaft

European Commission

Publisher

Wiley

Subject

Economics and Econometrics,Agricultural and Biological Sciences (miscellaneous)

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