Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: Long short-term memory
Document Type
Article
Publication Date
2-1-2022
Abstract
Drought modelling is an important issue because it is required for curbing or mitigating its effects, alerting the people to the its consequences, and water resources planning. This study investigates the capability of a deep learning method, long short-term memory (LSTM), in forecasting drought calculated from monthly rainfall data obtained from four stations of Iran. The outcomes of LSTM compared with extra-trees (ET), vector autoregressive approach (VAR) and multivariate adaptive regression spline (MARS) methods in forecasting four drought indices, SPI-3, SPI-6, SPI-9 and SPI-12, taking into account numerical criteria, root-mean-square errors (RMSE), Nash-Sutcliffe efficiency and correlation coefficient together with the visual methods, time variation graphs, scatter plots and Taylor diagrams. The overall results showed that the LSTM method performed superior to the ET, VAR and MARS in forecasting drought based on SPI-3, SPI-6, SPI-9 and SPI-12. The RMSE of ET, VAR and MARS was improved by about 17.1%, 12.8% and 9.6% for SPI-3, by 10.5%, 6.2% and 5% for SPI-6, by 7.3%, 4.1% and 6.2% for SPI-9 and by 22.2%, 27% and 10.6% for SPI-12 using LSTM. The MARS method was ranked as the second best, while the ET provided the worst results in forecasting drought based on SPI.
Keywords
Drought forecasting, Standard precipitation index, Deep learning, LSTM, Extra-trees, VAR, MARS
Divisions
sch_civ
Funders
None
Publication Title
Neural Computing and Applications
Volume
34
Issue
3, SI
Publisher
Springer London Ltd
Publisher Location
236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND