Mô phỏng dữ liệu dòng chảy bằng mô hình chi tiết hóa động lực kết hợp với thuật toán học máy: Áp dụng cho lưu vực sông Sài Gòn - Đồng Nai

Abstract

Spatial and temporal availability and reliability of hydrological data are substantial
contribution to the accuracy of watershed modeling. In this study, hydrological conditions are simulated using the hydrologic model-WEHY, whose data input are obtained from a hybrid downscaling technique to provide reliable and high temporal and spatial resolution hydrological data. The hybrid downscaling technique is coupled a hydro-climate and a machine learning models; wherein the global atmospheric reanalysis data, including ERA-Interim, ERA-20C, and CFSR are used for initial and boundary conditions of dynamical downscaling utilizing the Weather Research and Forecasting model (WRF). The machine learning model (ANN) then follows to further downscale the WRF outputs to a finer resolution over the studied watershed. An application of the combination of mentioned techniques is applied to third largest river basin in Vietnam, the Sai Gon – Dong Nai Rivers Basin. After the estimation of geomorphology and land cover within the watershed, WEHY’s calibration and validation are performed based on observation rainfall data. This result confirmed that the proposed method provide realiable data and it is possible to widely apply for other watersheds