Application of artificial intelligence hybrid models for meteorological drought prediction.

Drought is a prolonged dry period that has a serious impact on health, agriculture, economies, energy, and the environment. Thus, there have been numerous attempts to make this phenomenon more predictable for preventing the aforementioned effects. The present study aims to determine the best combination of input data sets and predict the Standardized Precipitation Evapotranspiration Index (SPEI) in 1,6, and 12-month time scales using Artificial Intelligence (AI) models (Multilayer Perceptron Neural Network (MLPNN), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree (EDT)), which all models are hybridized with a wavelet transformation at three synoptic stations named Ardebil, Khalkhal, and Moghan. To this end, monthly lags of precipitation, temperature, and SPEI were used in northwestern Iran from 1987 to 2018. The methods were classified into single parameter and multiparameter, and each sub-method was designed based on a combination of the parameters. Moreover, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Effective Factor Elimination Technique (EFET), and Feature Scaling (FS) were used to determine the best lags of parameters. In this regard, Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Nash–Sutcliffe Efficiency Index (NSE) were used as the statistical criteria to assess AI models, methods, and sub-methods. The results revealed that the sub-method (D) with W-MLPNN in the 1-month and 6-month time scales and the sub-method (D, P, T) with W-SVR in the 12-month time scale were the best models and sub-methods of this area, respectively. Moreover, based on the results, the efficiency of the AI was enhanced in longer time scales (more than the 6th month) and the longer the time scale, the more the number of lags (under the 6th month) in input data is decreased. [ABSTRACT FROM AUTHOR]