Abstract: Data fusion and assimilation can complement the advantages of multi-source measured data and model simulation to enhance monitoring accuracy and reliability.Changes in the water volume of the lower Lancang River have a thriving impact on downstream countries, but the water monitoring faces many challenges, such as the complexity of the basin and the impact of hydraulic engineering.In order to improve the accuracy and efficiency of water quantity monitoring, we proposed a nested multi-source data assimilation methodology coupled with the hydrodynamic model, which utilizes multi-source data such as manual measurement data, side-scan radar online monitoring data, and hydrodynamic numerical simulation data.Firstly, an accuracy improvement scheme of side-scan radar based on machine learning LASSO model was constructed by using artificial measured data.On this basis, a numerical simulation model of river hydrodynamics was constructed, and the parameters of hydrodynamic model were optimized by using the improved side-scan radar monitoring velocity, forming a nested fusion assimilation of multi-level and multi-source data.The aim was to improve the accuracy of water quantity simulation, extend the point observation data to the whole river, and expand the access range of the water quantity elements.The application in Yunjinghong Hydrological Station of Lancang River showed that: the accuracy improvement scheme based on machine learning LASSO model improved the accuracy of the side-scan radar monitoring system by 22.93% compared with the conventional method.The nested fusion assimilation mode of multi-level and multi-source data effectively improved the simulation accuracy of sectional flow, and the correlation coefficient in the verification period was 0.935.And the hydrological elements data such as water level, flow rate and velocity at any point in the modeled river channel were obtained.This study can provide a new methodology and technical support for the monitoring of water quantity of Lancang River.