The use of advanced computational methods in reliable river flow prediction (Международный научный журнал «Инженер», 2026)
Research on the Yangtze and Narmada Rivers has focused on predicting river flow and preventingflooding.The minimum prediction period of numerical computing methods is about a month ago,whichis also achieved because it is calculated too short for use in hydrological applications by combining adeep 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 YangtzeRiver. Monthly flow data from the Hankou hydrological station on the Yangtze River (January 1952 toDecember 2008) was selected to teach the model, and results were obtained for two years and ten yearsof continuous predictions of floods. To achieve a reliable and accurate result,modern programsCatBoost,LGBM, Random Forest and XGBoost are used.When checking the consistency of thecalculation results, the mean quadrad error (MSE),mean absolute error(MAE),root mean quadraderror(RMSE),root mean quadrad percentage error(RMSPE), normalized root mean quadraderror(NRMSE), and quadrad error (R2) indicators are used. Among the above models, Random Foresthas shown to be able to produce effective results even in complex conditions of hydrological prediction.