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Prediction of Rainfall as One of the Main Variables in Several Natural Disasters

  • Vahid Moosavi
Chapter
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 48)

Abstract

Rainfall is one of the main variables in several natural disasters such as, floods, drought, groundwater depletion and landslides. Therefore, development of robust models for rainfall forecasting is essential in environmental studies. The chief goal of this research is to use Group Method of Data Handling (GMDH) besides signal processing approaches to forecast rainfall in monthly time steps. To that end, three different signal processing approaches i.e. Ensemble empirical mode decomposition (EEMD), wavelet transform (WT) and wavelet packet transform (WPT) were used combined with GMDH model. Four stations were used to apply aforementioned modeling techniques. Results of this research showed that all three abovementioned signal processing approaches can enhance the ability of the GMDH model. The ability of EEMD-GMDH and wavelet packet-GMDH were relatively close to each other. However, wavelet packet-GMDH outperformed EEMD-GMDH model to some extent. The other important note was the effect of exogenous data on the ability of all models. It was shown that forecasting rainfall without using exogenous data does not produce acceptable results.

Keywords

Polynomial neural network GMDH Rainfall forecasting Wavelet Wavelet packet Ensemble empirical mode decomposition (EEMD) 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Watershed Management Engineering, Faculty of Natural ResourcesYazd UniversityYazdIran

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