Bridging technology transfer boundaries: Integrated cloud services deliver results of nonlinear process models as surrogate model ensembles

Environmental models are often essential to implement projects in planning, consulting and regulatory institutions. Research models are often poorly suited to such applications due to their complexity, data requirements, operational boundaries, and factors such as institutional capacities. This contribution enhances a modeling framework to help mitigate research model complexity, streamline data and parameter setup, reduce runtime, and improve model infrastructure efficiency. Using a surrogate modeling approach, we capture the intrinsic knowledge of a conceptual or process-based model into an ensemble of artificial neural networks. The enhanced modeling framework interacts with machine learning libraries to derive surrogate models for each model service. This process is secured using blockchain technology. After describing the methods and implementation, we present an example wherein hydrologic peak discharge provided by the curve number model is emulated with a surrogate model ensemble. The ensemble median values outperformed any individual surrogate model fit to the curve number model.