Precipitation forecasting using neural network model approach

Neural network constitutes a non-linear model requiring no statistical assumption. Along the development of which, neural network model has been frequently combined with time series and spatio temporal models. This current research combined neural network and spatio temporal models. One of spatio te...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2020-02, Vol.458 (1), p.12020
Main Authors: Iriany, A, Rosyida, D, Sulistyono, A D, Ruchjana, B N
Format: Article
Language:English
Subjects:
Covariance
Forecasting
Mathematical models
Neural networks
Weather forecasting
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Summary:Neural network constitutes a non-linear model requiring no statistical assumption. Along the development of which, neural network model has been frequently combined with time series and spatio temporal models. This current research combined neural network and spatio temporal models. One of spatio temporal models is GSTAR-SUR model. The weight projected in this current research is cross covariance normalized weight. This sort of weight is deemed suitable for data with high variability. The significant variable in GSTAR-SUR model containing cross covariance normalized weight was used as input layer of neural network model. The hidden layer made use of 10 neurons fulfilling the criteria of the lowest RMSE value and there was 1 neuron used as output. The data were in the form of 10-day precipitations in Junggo, Pujon, Tinjumoyo, and Ngujung, during the period of 2005 to 2014. This research has found out that NN-GSTAR-SUR model yielded better and more accurate forecasting, showing 2 value of 61.77%.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/458/1/012020