Genetic programming (GP) is a widely used machine learning (ML) algorithm that has been applied in water resources science and engineering since its conception in the early 1990s. However, similar to other ML applications, the GP algorithm is often used as a data fitting tool rather than as a model building instrument. We find this a gross underutilization of the GP capabilities. The most unique and distinct feature of GP that makes it distinctly different from the rest of ML techniques is its capability to produce explicit mathematical relationships between input and output variables. In the context of theory-guided data science (TGDS) which recently emerged as a new paradigm in ML with the main goal of blending the existing body of knowledge with ML techniques to induce physically sound models. Hence, TGDS has evolved into a popular data science paradigm, especially in scientific disciplines including water resources. Following these ideas, in our prior work, we developed two hydrologically informed rainfall-runoff model induction toolkits for lumped modelling and distributed modelling based on GP. In the current work, the two toolkits are applied using a different hydrological model building library. Here, the model building blocks are derived from the Sugawara TANK model template which represents the elements of hydrological knowledge. Results are compared against the traditional GP approach and suggest that GP as a rainfall-runoff model induction toolkit preserves the prediction power of the traditional GP short-term forecasting approach while benefiting to better understand the catchment runoff dynamics through the readily interpretable induced models.

遗传编程 (GP) 是一种广泛使用的机器学习 (ML) 算法，自 1990 年代初提出以来，一直应用于水资源科学和工程。但是，与其他 ML 应用程序类似，GP 算法通常用作数据拟合工具而不是模型构建工具。我们发现这是对 GP 功能的严重利用不足。GP 最独特和显着的特征使其与其他 ML 技术明显不同的是它能够在输入和输出变量之间产生明确的数学关系。在理论指导的数据科学 (TGDS) 的背景下，最近作为 ML 中的一种新范式出现，其主要目标是将现有知识体系与 ML 技术相结合，以产生物理上合理的模型。因此，TGDS 已经发展成为一种流行的数据科学范式，尤其是在包括水资源在内的科学学科中。遵循这些想法，在我们之前的工作中，我们开发了两个基于水文信息的降雨径流模型归纳工具包，用于基于 GP 的集总建模和分布式建模。在目前的工作中，使用不同的水文模型构建库来应用这两个工具包。这里的模型构建块源自代表水文知识要素的 Sugawara TANK 模型模板。