Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts
Atmospheric pressure; Bayesian networks; Climatology; Drought; Neural networks; Water management; Weather forecasting; ANN; Bayesian model averaging; Meteorological drought; Optimization algorithms; Salp swarms; Sine-cosine algorithm; Standardized precipitation index; Swarm algorithms; Water resourc...
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Springer Science and Business Media Deutschland GmbH
2023
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my.uniten.dspace-268462023-05-29T17:37:10Z Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts Achite M. Banadkooki F.B. Ehteram M. Bouharira A. Ahmed A.N. Elshafie A. 6506127655 57201068611 57113510800 57421498900 57214837520 16068189400 Atmospheric pressure; Bayesian networks; Climatology; Drought; Neural networks; Water management; Weather forecasting; ANN; Bayesian model averaging; Meteorological drought; Optimization algorithms; Salp swarms; Sine-cosine algorithm; Standardized precipitation index; Swarm algorithms; Water resources management; Water striders; Particle swarm optimization (PSO); accuracy assessment; algorithm; artificial neural network; Bayesian analysis; drought; El Nino-Southern Oscillation; North Atlantic Oscillation; optimization; Pacific Decadal Oscillation; precipitation assessment; Southern Oscillation; weather forecasting Forecasting drought is essential for water resource management when policymakers encounter a water shortage and high demand. This research utilizes the Bayesian averaging model (BMA) based on multiple hybrid artificial neural network models including ANN- water strider algorithm (WSA), ANN-particle swarm optimization (ANN-PSO), ANN-salp swarm algorithm (ANN-SSA), and ANN-sine cosine algorithm (ANN-SCA) to forecast standardized precipitation index as one of the most important indices of drought. The models were used to forecast Standardized Precipitation Index (SPI) SPI (1), SPI (3), SPI (6), and SPI (12) in the Wadi Ouahrane basin of Algeria. The WSA, SSA, SCA, and PSO were applied to set model parameters of the ANN model. The inputs were lagged El Ni�o�Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), North Atlantic oscillation index (NAO), and southern oscillation index (SOI). The gamma test was integrated with WSA to identify the best input scenario for forecasting drought. The BMA for forecasting SPI (1) improved the MAE attained by the ANN-WSA, ANN-SSA, ANN-SCA, ANN-PSO, and ANN models 26, 33, 38, 42, and 46%, respectively in the testing level. The MAE of BMA for forecasting SPI (6) was 40, 42, 46, 48, and 62% lower than those of ANN-WSA, ANN-SSA, ANN-SCA, ANN-PSO, and ANN-PSO. Also, the BMA and ANN-WSA had the best accuracy among other models for forecasting SPI (6) and SPI (12). This study indicated that the WSA, SSA, SCA, and PSO improved the accuracy of the ANN models for forecasting drought. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:37:10Z 2023-05-29T09:37:10Z 2022 Article 10.1007/s00477-021-02150-6 2-s2.0-85123260591 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123260591&doi=10.1007%2fs00477-021-02150-6&partnerID=40&md5=1b3c39f26a25b01d7b2216c648f440ab https://irepository.uniten.edu.my/handle/123456789/26846 36 7 1835 1860 Springer Science and Business Media Deutschland GmbH Scopus |
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Atmospheric pressure; Bayesian networks; Climatology; Drought; Neural networks; Water management; Weather forecasting; ANN; Bayesian model averaging; Meteorological drought; Optimization algorithms; Salp swarms; Sine-cosine algorithm; Standardized precipitation index; Swarm algorithms; Water resources management; Water striders; Particle swarm optimization (PSO); accuracy assessment; algorithm; artificial neural network; Bayesian analysis; drought; El Nino-Southern Oscillation; North Atlantic Oscillation; optimization; Pacific Decadal Oscillation; precipitation assessment; Southern Oscillation; weather forecasting |
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6506127655 |
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6506127655 Achite M. Banadkooki F.B. Ehteram M. Bouharira A. Ahmed A.N. Elshafie A. |
| format |
Article |
| author |
Achite M. Banadkooki F.B. Ehteram M. Bouharira A. Ahmed A.N. Elshafie A. |
| spellingShingle |
Achite M. Banadkooki F.B. Ehteram M. Bouharira A. Ahmed A.N. Elshafie A. Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts |
| author_sort |
Achite M. |
| title |
Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts |
| title_short |
Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts |
| title_full |
Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts |
| title_fullStr |
Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts |
| title_full_unstemmed |
Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts |
| title_sort |
exploring bayesian model averaging with multiple anns for meteorological drought forecasts |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
| _version_ |
1806425517820215296 |
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13.211869 |