Improving accuracy of neuro fuzzy and support vector regression for drought modelling using grey wolf optimization

ABSTRACT

In this study the capability of two advanced hybrid artificial intelligence-based methods is investigated in modelling meteorological droughts based on the standardized precipitation index (SPI) for various time windows. The outcomes are compared with adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA). The best-fitted distribution functions are found to vary with respect to stations and time windows. For Canakkale, Istanbul and Tekirdag stations, the correlation coefficients (CC) of hybridized method of ANFIS with gray wolf optimization (ANFIS-GWO) and ARIMA are in the range of 0.88–0.94, 0.88–0.96 and 0.86–0.94, respectively. The performance index also showed that the ANFIS-GWO provides superior accuracy in modelling droughts, with the minimum value (PI = 0.78) for SPI12 of Canakkale station. Forward-chaining cross-validation and the P value of the Chi-squared test also confirm that ANFIS-GWO is the superior model, in which there is not a significant difference between the trend of the predicted categories and that of the real categories for all stations and SPIs.

KEYWORDS:

• drought
• AI models
• GWO
• forecast
• prediction matrix
• traditional models

Editor A. Fiori; Associate Editor D. Rivera

Editor A. Fiori; Associate Editor D. Rivera

Data availability

The data used in this research is available from the corresponding author upon reasonable request.

Code availability

The code for this research is available from the corresponding author upon reasonable request.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

The authors confirm that this article had no funding.