000K utf8 1100 2020$c2020-01-07 1500 eng 2050 urn:nbn:de:gbv:wim2-20200213-40796 2051 10.1109/ACCESS.2020.2964584 3000 Nabipour, Narjes 3010 Dehghani, Majid 3010 Mosavi, Amir 3010 Shamshirband, Shahaboddin 4000 Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks$hIEEE [Nabipour, Narjes] 4030 $nIEEE 4209 Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40. 4950 https://doi.org/10.1109/ACCESS.2020.2964584$xR$3Volltext$534 4950 https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200213-40796$xR$3Volltext$534 4961 https://www.db-thueringen.de/receive/dbt_mods_00061024 5051 500 5550 Deep learning 5550 Hydrological drought 5550 hydrology 5550 Machine learning 5550 Maschinelles Lernen 5550 precipitation