@article{DehghaniSalehiMosavietal.,
  author    = {Dehghani, Majid and Salehi, Somayeh and Mosavi, Amir and Nabipour, Narjes and Shamshirband, Shahaboddin and Ghamisi, Pedram},
  title     = {Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices},
  series = {ISPRS, International Journal of Geo-Information},
  volume    = {2020},
  journal   = {ISPRS, International Journal of Geo-Information},
  number    = {Volume 9, Issue 2, 73},
  publisher = {MDPI},
  doi       = {10.3390/ijgi9020073},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200128-40740},
  pages     = {23},
  abstract  = {Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Ni{\~n}o-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100\%. However, the seasonal precipitation may increase more than 100\% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{NabipourDehghaniMosavietal.,
  author    = {Nabipour, Narjes and Dehghani, Majid and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks},
  series = {IEEE Access},
  volume    = {2020},
  journal   = {IEEE Access},
  number    = {volume 8},
  publisher = {IEEE},
  doi       = {10.1109/ACCESS.2020.2964584},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40796},
  pages     = {15210 -- 15222},
  abstract  = {Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}