ORIGINAL PAPER Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting Majid Dehghani 1 & Bahram Saghafian 2 & Firoozeh Rivaz 3 & Ahmad Khodadadi 3 Received: 5 October 2016 /Accepted: 18 April 2017 # Saudi Society for Geosciences 2017 Abstract In this study, application of a class of stochastic dynamic models and a class of artificial intelligence model is reported for the forecasting of real-time hydrological droughts in the Black River basin in the USA. For this pur- pose, the Standardized Hydrological Drought Index (SHDI) was adopted in different time scales to represent the hydrolog- ical drought index. Six probability distribution functions (PDF) were fitted to the discharge time series to obtain the best fit for SHDI calculation. Then, a dynamic linear spatio- temporal model (DLSTM) and artificial neural network (ANN) were used to forecast SHDI. Although results indicated that both models were able to forecast SHDI in different time scales, the DLSTM was far superior in longer lead times. The DLSTM could forecast SHDI up to 6 months ahead while ANN was only capable of forecasting SHDI up to 2 months ahead appropriately. For short lead times (1–6 months), the DLSTM has performed nearly perfect in test phase and CE oscillates between 0.97 and 0.86 while for ANN modeling, CE is between 0.72 and 0.07. However, the performance of DLSTM and ANN reduced considerably in medium lead times (7–12 months). Overall, the DLSTM is a powerful tool for appropriately forecasting SHDI at short time scales; a major advantage required for drought early warning systems. Keywords Hydrologicaldrought . DLSTM . Ann . Forecast . SHDI . Drought early warning system Introduction Drought is a natural dynamic phenomenon whose character- istics vary in space and time. Drought is attributed to the temporal variation of hydro-meteorological time series. There is no universal definition of drought and most of the definitions are study-specific. Generally speaking, drought is eventually sensed after a prolonged precipitation shortage resulting in insufficient water to meet the demands in a period of time. In a way, the difference between water availability and water demand determines the drought severity that humans feel. There are several drought classifications. Wilhite and Glantz (1985) classified droughts into meteorological, hydro- logical, agricultural, and socioeconomic. Mishra and Singh (2010) proposed to add groundwater drought as another type of drought. Lack of precipitation during a period of time is known as meteorological drought while insufficient surface and ground water compared with long-term values are known as hydrological drought (Mishra and Singh 2010). Drought assessment, including monitoring and forecasting, is vital to efficient water resources planning and management. Researchers have been trying to develop various tools to in- vestigate aspects of droughts. Drought impacts, monitoring, forecasting, and mitigation have been widely investigated by * Majid Dehghani m.dehghani@vru.ac.ir Bahram Saghafian b.saghafian@gmail.com Firoozeh Rivaz f_rivaz@sbu.ac.ir Ahmad Khodadadi a_khodadadi@sbu.ac.ir 1 Technical and Engineering Department, Faculty of Civil and Environmental Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Islamic Republic of Iran 2 Technical and Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran 3 Department of Statistics, Shahid Beheshti University, Tehran, Islamic Republic of Iran Arab J Geosci (2017) 10:266 DOI 10.1007/s12517-017-2990-4