Forecasting drought using neural network approaches with transformed time series data
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 48(2021), 13-15 vom: 14., Seite 2591-2606 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics |
| Schlagworte: | Journal Article ANN Drought index SPI machine learning nonlinear auto-regressive wavelet |
| Zusammenfassung: | © 2020 Informa UK Limited, trading as Taylor & Francis Group. Drought is one of the important and costliest disaster all over the world. With the accelerated progress of climate change, its frequency of occurrence and negative impacts are rapidly increasing. It is crucial to initiate and sustain an early warning system to monitor and predict the possible impacts of future droughts. Recently, with the rise of data driven models, various case studies are conducted by using Machine Learning algorithms instead of using pure statistical approaches. The main goal of this paper is to conduct a drought forecasting study for a weather station located in Marmara Region. For that purpose, firstly, widely used univariate drought index, Standardized Precipitation Index is calculated for Bursa station. Thereafter, both the historical information retrieved from time series data and its wavelet transformation are considered to investigate Nonlinear Auto-Regressive and Nonlinear Auto-Regressive with External Input (NARX) type Neural Network (NN) models. According to a pool of Goodness-of-Fit (GOF) tests, the forecasting performance of the models with various number of hidden neurons are compared. The recent findings of the study showed that considering the data with its wavelet transformation under (NARX-NN) has benefits to increase the capacity of forecasting the drought index |
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| Beschreibung: | Date Revised 16.06.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0266-4763 |
| DOI: | 10.1080/02664763.2020.1867829 |