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• W1980337479 abstract "The prediction of rainfall is the premise of responding to water resources management and flood defense. The hydrological system is complex, and rainfall time series as a member of it has characteristics of being nonlinear and nonstationary, so accurate rainfall prediction still a much difficult job at present. The paper proposes a conjunction model named EMD-KNN for forecasting annual average rainfall. The model is improved by combining two methods, empirical mode decomposition (EMD) and the K-nearest neighbor bootstrap regressive model (K-NN). It is applied to case studies of forecasting annual average rainfall for Nanjing city and the Dahuofang reservoir basin. Where Nanjing city is water-rich area in East China, Dahuofang reservoir basin is water-deficient area in Northeast China. Three performance evaluation measures results revealed that the EMD-KNN model reduces the prediction mean absolute error (MAE), mean relative error (MRA), and root-mean square error (RMSE) with respect to the single K-NN model by almost 50% each, so the suggested model can effectively improve the forecast accuracy of single K-NN in forecasting the annual average rainfall." @default.
• W1980337479 created "2016-06-24" @default.
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• W1980337479 date "2013-11-01" @default.
• W1980337479 modified "2023-10-05" @default.
• W1980337479 title "EMD-KNN Model for Annual Average Rainfall Forecasting" @default.
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• W1980337479 doi "https://doi.org/10.1061/(asce)he.1943-5584.0000481" @default.
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