Drought prediction using hybrid soft-computing methods for semi-arid region

Başakın E. E. , Ekmekcioğlu Ö. , Özger M.

MODELING EARTH SYSTEMS AND ENVIRONMENT, 2020 (Journal Indexed in ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2020
  • Doi Number: 10.1007/s40808-020-01010-6
  • Keywords: Self-calibrated PDSI, Drought, Fuzzy logic, Prediction, EMD, INDEX, MODEL


Drought is one of the most significant natural disaster and prediction of drought is a key aspect in effective management of water resources and reducing the effect of a drought with preliminary studies plays significant role. In this study, we predicted one of the meteorological drought indices, the self-calibrated Palmer Drought Severity Index (sc-PDSI), values for Adana, Turkey. First, we used adaptive neuro fuzzy inference system (ANFIS) as a standalone technique to predict sc-PDSI. Second, we used empirical mode decomposition (EMD) as a pre-processing technique to decompose the sc-PDSI time series into the sub-series and applied ANFIS to each sub-series. Following the prediction, results are summed each other and final prediction of the hybrid EMD-ANFIS method is obtained. Within the scope of the study, 1, 3and 6-months lead time sc-PDSI values are predicted. We utilized the mean square error (MSE) and Nash-Sutcliffe efficiency coefficient (NSE) as performance indicators in order to perform statistical evaluation. For ANFIS, we obtained NSE = 0.52 and NSE = 0.17 for 3-month and 6-month lead times, respectively. Also, NSE values are obtained as 0.81 and 0.77 for the hybrid model in 3-month and 6-month lead time predictions, respectively. The results revealed that the hybrid EMD-ANFIS model outperforms the standalone ANFIS model. Also, the predicted and actual sc-PDSI series investigated according to the statistical distributions. At last, error histograms of both predicted and actual series are compared according to the Kolmogorov-Smirnov test and the p values are calculated. The results illustrated the predictions are statistically significant.