Huu Nam Nguyen1 , Thuy-Anh Nguyen2 , Hai-Bang Ly2 , Van Quan Tran This email address is being protected from spambots. You need JavaScript enabled to view it.2 , Long Khanh Nguyen2 , Minh Viet Nguyen1 , and Canh Tung Ngo3

1Institute for Hydropower and Renewable Energy, Hanoi, Vietnam
2University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, 100000, Vietnam
3Hydraulic Construction Institute, Hanoi, Vietnam


 

Received: September 8, 2020
Accepted: December 23, 2020
Publication Date: June 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

 


Download Citation: ||https://doi.org/10.6180/jase.202106_24(3).0012  


ABSTRACT


In this study, the main goal is to develop a model using artificial intelligence (AI) based on the artificial neural network (ANN) for the prediction of daily and monthly rainfall. The authors compare the prediction accuracy of between daily and monthly rainfall, using meteorological parameters as input information (temperature, dew point, humidity, pressure, visibility, and wind speed). Validation of the developed model is achieved using various quantitative evaluation criteria such as correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), which are respectively 0.8063, 0.2487, and 0.0932 for the daily rainfall, and 0.8012, 0.0731 and 0.0578 for monthly rainfall. A comparison is then performed, which shows a higher prediction accuracy of monthly than daily rainfall. These reliable results could help in constructing a soft computing tool to predict accurately and quickly the daily and monthly rainfall.


Keywords: rainfall prediction, artificial intelligence, artificial neural network, daily rainfall, monthly rainfall.


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