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Babak Mohammadi

Doktorand

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A novel hybrid dragonfly optimization algorithm for agricultural drought prediction

Författare

  • Pouya Aghelpour
  • Babak Mohammadi
  • Saeid Mehdizadeh
  • Hadigheh Bahrami-Pichaghchi
  • Zheng Duan

Summary, in English

Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM’s parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM’s accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publiceringsår

2021-04

Språk

Engelska

Sidor

2459-2477

Publikation/Tidskrift/Serie

Stochastic Environmental Research and Risk Assessment

Volym

35

Dokumenttyp

Artikel i tidskrift

Förlag

Springer

Ämne

  • Oceanography, Hydrology, Water Resources
  • Physical Geography
  • Water Engineering

Nyckelord

  • Hydrological modeling
  • Machine learning
  • Hydroinformatics
  • Drought
  • Stochastic model
  • Optimization Algorithms

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1436-3240