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Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

  • 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 DroughtHydrological 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.show moreshow less

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Metadaten
Document Type:Article
Author: Narjes NabipourORCiD, Majid DehghaniORCiD, Amir MosaviORCiD, Shahaboddin ShamshirbandORCiD
DOI (Cite-Link):https://doi.org/10.1109/ACCESS.2020.2964584Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200213-40796Cite-Link
URL:https://ieeexplore.ieee.org/document/8951168
Parent Title (German):IEEE Access
Publisher:IEEE
Language:English
Date of Publication (online):2020/01/29
Date of first Publication:2020/01/07
Release Date:2020/02/13
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 8
Pagenumber:13
First Page:15210
Last Page:15222
Tag:Deep learning; Hydrological drought; Machine learning; hydrology; precipitation
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:500 Naturwissenschaften und Mathematik
BKL-Classification:54 Informatik
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)