Drought forecasting using artificial neural networks and time series of drought indices

By Saeid Morid, V. Smakhtin, K. Bagherzadeh. 2007

334 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/8512522ef92cefbefeb8a93a166faab59420f252

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In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, background): “A lot of research papers use artificial neural network (ANNs) for drought forecasting (Mishra and Desai 2006; Morid et al. 2007; Barros and Bowden 2008; Bacanli et al. 2008; Karamouz et al. 2009; Marj and Meijerink 2011; Mishra and Nagarajan 2012).”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, methodology): “ANNs have been used in a number of studies as a drought forecasting tool (Mishra and Desai, 2006; Morid et al., 2007; Bacanli et al., 2008; Barros and Bowden, 2008; Cutore et al., 2009; Karamouz et al., 2009; Marj and Meijerink, 2011; Mishra and Nagarajan, 2012).”
Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran) (2013, background): “…rainfall-runoff process (Jeong and Kim 2005; Kumar et al. 2005), hydrologic time series modeling (Jain and Kumar 2007), sediment concentration estimation (Nagy et al. 2002), estimation of heterogeneous aquifer parameters (Mantoglou 2003), runoff, and sediment yield modeling (Agarwal et al. 2006).”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “In addition, there are limited applications of wavelet-hybrid models for drought studies (Kim and Valdés 2003; Morid et al. 2007; Belayneh and Adamowski 2012; Masinde 2013; Belayneh et al. 2016) and no such study has explored the W-ELM for drought forecasting.”
Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand (2020, background): “Recently, the principle of EDI was extended to the monthly time scale for meteorological drought assessment (Morid et al. 2007; Dogan et al. 2012; Jain et al. 2015).”
Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia (2015, methodology): “The PDO is created using theUKMOHistorical SST dataset for 1900–81, Reynolds Optimally Interpolated SST (V1) (Morid et al., 2007) for January 1982–Dec 2001 and OI.”