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Storm floods occur frequently and have complex characteristics in arid mountainous areas, which for a long time has been a weak link in flood forecasting. The application of an artificial intelligent model and physically-based hydrological model has some limitations on flood forecasting in arid mountainous areas with scarce data. In this article, the ANN model and Muskingum-Cunge method are combined to propose an intelligent networking model for flood forecasting (FFIN model) in arid mountainous areas with scarce data, which BR-ANN model is used to forecast the flood in the catchment sub-basin with runoff data, while the General Regression Neural Network model is used to carry out flood parallel forecast in the catchment sub-basin without runoff data. The Muskingum-Cunge method is used to connect the sub-basins and form a confluence network, so as to simulate the flood routing process in river. The verification and comparison results in study area show that the FFIN model has a superior overall forecasting ability. For the forecasting period, the evaluation index Kling-Gupta efficiency is 0.88, Nash efficiency coefficient is 0.982 and forecasting deviation of flood peak flow is 7.15%. The FFIN model can be effectively applied to flood forecasting in arid mountainous areas with scarce runoff data.

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