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Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory

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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.

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Authors and Affiliations

  1. Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan, Iran

    Alireza Docheshmeh Gorgij

  2. Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Meysam Alizamir

  3. Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia

    Ozgur Kisi

  4. Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Ahmed Elshafie

  5. National Water Center, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates

    Ahmed Elshafie

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  1. Alireza Docheshmeh Gorgij
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  2. Meysam Alizamir
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  3. Ozgur Kisi
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  4. Ahmed Elshafie
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Correspondence to Meysam Alizamir or Ozgur Kisi.

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Docheshmeh Gorgij, A., Alizamir, M., Kisi, O. et al. Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory. Neural Comput & Applic 34, 2425–2442 (2022). https://doi.org/10.1007/s00521-021-06505-6

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