Comment on "Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China" by Yuhu Zhang, Huirong Yang, Hengjian Cui, and Qiuhua Chen, in Natural Resources Research DOI: 10.1007/s11053-019-09512-6

Başakın E. E., Ekmekcioğlu Ö.

NATURAL RESOURCES RESEARCH, cilt.29, sa.2, ss.1465-1467, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Editöre Mektup
  • Cilt numarası: 29 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11053-020-09638-y
  • Dergi Adı: NATURAL RESOURCES RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Environment Index, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1465-1467
  • Anahtar Kelimeler: Artificial neural network, Wavelet transform, Support vector machine, Drought, Forecasting, WAVELET
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

We thank Zhang et al. (Nat Resour Res, 2019. 10.1007/s11053-019-09512-6) for investigating the accuracy of artificial intelligence techniques in the prediction of drought in China. In the paper by Zhang et al. (2019), two data-driven models, namely artificial neural network and support vector machine, and autoregressive integrated moving average (ARIMA) model were established to estimate standardized precipitation evapotranspiration index (SPEI) values. In that paper, temperature and precipitation values were used as independent variables to predict SPEI. They stated that ARIMA models give higher accuracy in the prediction of SPEI values. Here, not only some of the missing points and deficiencies in the original publication, but also improvements that can be made in future studies, were mentioned. In addition, several points are introduced in order to make these points more clarified for potential readers.