Rainfall intensity has a significant impact on urban drainage infrastructures and the precipitation forecast therefore remains essential in urban areas. In this study, a prediction model using Nonlinear Autoregressive Neural Networks (NANN) was proposed to forecast 48-hour-ahead the rainfall intensity. The proposed NANN model, which is based on a precipitation data of five-year time series, was constructed and validated using various parameters such as Coefficient of Determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results exhibited a high statistical correlation between the outputs of NANN model and the measured data for 48 hour ahead prediction, i.e. R2=0.8998, RMSE=3.2909 and MAE=1.8672. This indicates that the developed model is very promising for precipitation forecasting and could contribute to improve the urban drainage systems.


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    Titel :

    Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network


    Weitere Titelangaben:

    Lecture Notes in Civil Engineering


    Beteiligte:
    Ha-Minh, Cuong (Herausgeber:in) / Dao, Dong Van (Herausgeber:in) / Benboudjema, Farid (Herausgeber:in) / Derrible, Sybil (Herausgeber:in) / Huynh, Dat Vu Khoa (Herausgeber:in) / Tang, Anh Minh (Herausgeber:in) / Le, Tien-Thinh (Autor:in) / Pham, Binh Thai (Autor:in) / Ly, Hai-Bang (Autor:in) / Shirzadi, Ataollah (Autor:in)


    Erscheinungsdatum :

    2019-10-11


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch