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.

环境模型对于在规划、咨询和监管机构中实施项目通常是必不可少的。由于其复杂性、数据要求、操作边界以及机构能力等因素，研究模型通常不太适合此类应用。这一贡献增强了建模框架，以帮助降低研究模型的复杂性、简化数据和参数设置、减少运行时间并提高模型基础架构效率。使用代理建模方法，我们将概念或基于过程的模型的内在知识捕获到人工神经网络的集合中。增强的建模框架与机器学习库交互，为每个模型服务派生代理模型。此过程使用区块链技术进行保护。在描述了方法和实现之后，我们提出了一个示例，其中使用代理模型集合来模拟曲线数模型提供的水文峰值流量。整体中值优于拟合曲线数模型的任何个体替代模型。