Open AccessA hybrid wavelet neural network (HWNN) for forecasting rainfall using temperature and climate indices
Author(s) -
Meysam Ghamariadyan,
Monzur Alam Imteaz,
Fatemeh Mekanik
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/351/1/012003
Subject(s) - artificial neural network , wavelet , wavelet transform , environmental science , climatology , computer science , meteorology , geology , artificial intelligence , geography
Rainfall forecasting plays an important role in water resources management and also for controlling the unusual events related to the rainfall. This study aims to forecast monthly rainfall from antecedent monthly rainfall, temperature and climate indices using a hybrid wavelet neural network (HWNN) model. The discrete wavelet transform is used incorporation with a conventional ANN model. The skilfulness of the proposed model is compared with the observed rainfall and the ANN model. The results show that the HWNN model provides a good fit with the observed rainfall data particularly in facing the extreme rainfall. The decomposed sub-series obtained by wavelet transform can extract invaluable information which is enormously useful for future rainfall prediction. The results confirm that the hybrid model considerably improves the neural network ability to predict future rainfall.