(untitled) (Международный научный журнал «Инженер», 2026)
Research on the Yangtze and Narmada Rivers has focused on predicting river flow and preventing flooding.The minimum prediction period of numerical computing methods is about a month ago,which is also achieved because it is calculated too short for use in hydrological applications by combining a deep neural network - empirical mode degradation (EMD) algorithm and a encoder-decoder Long-Short- Term Memory (En-de-LSTM) architecture to predict river flow in a study conducted on the Yangtze River. Monthly flow data from the Hankou hydrological station on the Yangtze River (January 1952 to December 2008) was selected to teach the model, and results were obtained for two years and ten years of continuous predictions of floods. To achieve a reliable and accurate result,modern programs CatBoost,LGBM, Random Forest and XGBoost are used.When checking the consistency of the calculation results, the mean quadrad error (MSE),mean absolute error(MAE),root mean quadrad error(RMSE),root mean quadrad percentage error(RMSPE), normalized root mean quadrad error(NRMSE), and quadrad error (R2) indicators are used. Among the above models, Random Forest has shown to be able to produce effective results even in complex conditions of hydrological prediction.