Interbasin Water Transfer (IWT) is often a complex decision-making process that depends on factors ranging from hydro-meteorological conditions to socio-economic pressures. Hydrologic modelling is particularly challenging under these circumstances, requiring accurate quantitative information which may not always be available. This study proposes a methodological framework to simulate IWT flow contributions in the absence of observational data by introducing a coupled machine learning–hydrologic modelling approach. The proposed methodology employs a hydrologic model to simulate the rainfall-runoff process of a watershed, while a machine learning algorithm is used to simulate the decision-making process of IWTs. Methods are illustrated by simulating the hydrologic balance of the Dese-Zero River Basin (DZRB), a highly artificially modified catchment located in North-East Italy. Results suggest the proposed methodological framework can successfully simulate the complex water flow dynamics of the studied watershed and be a useful instrument to support complex scenario analysis under IWTs data scarce conditions.

跨流域调水（IWT）通常是一个复杂的决策过程，取决于从水文气象条件到社会经济压力的各种因素。在这种情况下，水文建模尤其具有挑战性，需要精确的定量信息，而这些信息可能并不总是可用。这项研究提出了一种方法框架，通过引入一种结合机器学习-水文建模方法来模拟在没有观测数据的情况下内河流动的贡献。所提出的方法采用水文模型来模拟流域的降雨-径流过程，而使用机器学习算法来模拟内河运输的决策过程。通过模拟Dese-Zero流域（DZRB）的水文平衡来说明方法，位于意大利东北部的经过高度人工改造的集水区。结果表明，所提出的方法框架可以成功地模拟所研究流域的复杂水流动力学，并且是支持在内河航运数据稀缺条件下进行复杂情景分析的有用工具。