## 1. Introduction

Drought is a natural disaster imposing significant impact on socio-economic, agricultural, and environmental domains. Although the definition of drought is not universal, its occurrence is usually attributed to a long and sustained period of scarce water availability (Dracup *et al.*, 1980; Redmond, 2002). Drought imposes severe consequences on all affected regions, in particular arid and semi-arid regions. Different drought classifications have been proposed. Wilhite and Glantz (1985) classified droughts in four categories: meteorological, agricultural, hydrological, and socio-economic. Hydrological drought emerges with a lag after meteorological drought and is generally defined as a period of time in which the amount of available water, river discharge, or reservoir level is less than the normal condition.

Hydrological drought forecasting helps decision makers to lay out mitigation measures within the context of water resources management. Traditionally, statistical time series models have been used for drought forecasting. Simple multiple regression and autoregressive moving average (ARMA) models are typical forecasting models. However, such models are basically linear models assuming that data are stationary. These models have limited ability to capture nonlinearities in the hydrologic data. Thus, hydrologists tend to consider alternative forecasting tools when nonlinearity and nonstationarity exist in the data (Kim and Valdes, 2003).

Most of the reported researches deal with direct streamflow forecasting rather than drought index forecasting. Coulibaly *et al.* (2000) introduced an early stopped training approach (STA) to train multi-layer feed-forward neural networks (FFNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt back propagation (LMBP) and cross-validation technique to avoid underfitting/overfitting on FFNN training and enhance generalization performance. Overall, the results showed that the proposed method is effective for improving prediction accuracy.

Wang *et al.* (2006) predicted 1–10 d ahead stream discharge using three types of artificial neural networks. They used six preprocessing methods for input data. Results indicated that standardized data can improve the networks performance.

Aqil *et al.* (2006) evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. For comparison, a multiple linear regression analysis performed by the Citarum River Authority was also examined using various statistical indices. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13 and 10%, respectively.

Modarres (2007) applied a multiplicative seasonal autoregressive integrated moving average (SARIMA) model to forecast monthly streamflow of Zayandehrud River in western Isfahan province, Iran. Observed and forecasted streamflow showed a drought period but with different return periods.

Fernandez *et al.* (2009) applied ARIMA model to forecast monthly streamflow in a watershed in Spain. After forecasting 12 leading month streamflow, three drought thresholds including streamflow mean, monthly streamflow mean, and standardized streamflow index were chosen. Both observed and forecasted streamflow showed no drought evidence in this basin.

Raziei *et al.* (2010) investigated space–time variability of hydrological drought in Iran. They prepared precipitation dataset for the period of 1948–2007 using NCEP/NCAR and GPCC dataset. The aim was detection of long-term trends in drought/wetness time series. Results indicated that there is a good agreement in southeastern and north-western regions, while discrepancies occur for central and Caspian sea regions for two datasets.

Tabari *et al.* (2012) used Streamflow Drought Index (SDI) for assessment of hydrological drought in northwest of Iran and reported that streamflow volume did not follow the normal distribution. Thus, they tested some other distributions and finally applied lognormal distribution to generate the SDI time series. Results indicated that all the stations experienced extreme droughts, especially in the last decade.

In this research, using the concept and methodology of Standardized Precipitation Index (SPI) meteorological drought index, Standardized Hydrological Drought Index (SHDI) is first introduced for analysis of hydrological droughts. Then, as a first scenario, ANN model is trained with the SHDI time series and later applied to forecast the drought index. In the second scenario, ANN model is trained with the streamflow discharge time series to forecast the discharge first. Then, the forecasted discharge time series is converted to the SHDI and the results of the two scenarios are compared. Finally, the uncertainties of discharge and drought index forecasts are investigated using a Monte-Carlo simulation approach.