Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts

Achite, Mohammed; Banadkooki, Fatemeh Barzegari; Ehteram, Mohammad; Bouharira, Abdelhak; Ahmed, Ali Najah; El‐Shafie, Ahmed

doi:10.1007/s00477-021-02150-6

Cited by 25 publications

(6 citation statements)

References 58 publications

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“…Various validation methods are suggested in this regard, such as experimental tests, numerical simulations, analytical solutions, or comparative studies [48,49]. Furthermore, it is recommended to use the Bayesian model averaging approach to overcome the model uncertainty [50,51]. Trafc safety can be greatly compromised by pavement distress and surface characteristics, which can afect the drivers' lanechanging behavior and cause accidents [52,53].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Severity of Accidents on Rural Roads Using Statistical and Artificial Neural Network Methods

Habibzadeh,

Ayar,

Mirabimoghaddam

et al. 2023

Journal of Advanced Transportation

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This study assesses the relationship that existed between various variables and their subvariables on rural roads in Qom, Iran, using statistical analysis and calculates the relationship between the considered factors and accident severity. A logit model was applied to determine the factors affecting the severity of accidents. In addition, two artificial neural network (ANN) models were developed using two kinds of learning methods to train neurons to select the best result. The results of modeling and analysis of accidents using various techniques revealed that each technique, depending on its purpose, examined the severity of accidents from a different point of view and represented various outcomes. Finally, the performance of the proposed models was validated utilizing other mathematical models. As a result, putting the output results together, the best measures can be suggested to increase the safety of people on rural roads. The outcomes of this study may aid these service providers in strategic planning and policy framework.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Severity of Accidents on Rural Roads Using Statistical and Artificial Neural Network Methods

Habibzadeh,

Ayar,

Mirabimoghaddam

et al. 2023

Journal of Advanced Transportation

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“… Sfetsos & Coonick [ 9 ]; Nguyen, Pham, Duong & Vu [ 10 ]; Malik, Gehlot, Singh, Gupta & Thakur [ 11 ] Multilayer Perceptron (MLP) and Feed-Forward Back-Propagation Networks Used to predict solar irradiance on horizontal surfaces with a net error significantly lower than some linear methods. Gardner & Dorling [ 12 ]; Paoli, Voyant, Muselli, & Nivet [ 13 ]; Achite, Banadkooki, Ehteram, Bouharira, Ahmed & Elshafie [ 14 ] Precision evaluation using RMSE, nRMSE, and MAPE Mixed results in predictions, hovering around 20–30 % accuracy in some cases. Ben Ammar, Ben Ammar & A. Oualha [ 15 ]; Notton, Voyant, Fouilloy, Duchaud & Nivet [ 16 ]; Benali, Notton, Fouilloy, Voyant, Dizene [ 17 ]; Gao, Huang & Shi [ 18 ] Random Forest in predicting global radiance nRMSE error around 20–30 % for global radiance predictions.…”

Section: Related Workmentioning

confidence: 99%

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Travieso-González,

Celada-Bernal,

Lomoschitz

et al. 2024

Heliyon

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“…The PSO algorithm is known for its robustness and its ability to exchange information between particles. Achieving a good balance between exploration and exploitation is one of the advantages of PSO (Achite et al, 2022). A PSO begins by determining the particle's position and velocity (Ehteram et al, 2021).…”

Section: Particle Swarm Optimization Algorithm (Psoa)mentioning

confidence: 99%

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Mirzakhani¹,

Ghanbari-Adivi²,

Fattahi³

et al. 2022

Preprint

Self Cite

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Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH- honey badger algorithm (HBA), GMDH- rat swarm algorithm (RSOA), GMDH- sine cosine algorithm (SCA), and GMDH- particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash–Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90%. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.

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Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts

Cited by 25 publications

References 58 publications

Analysis of the Severity of Accidents on Rural Roads Using Statistical and Artificial Neural Network Methods

Analysis of the Severity of Accidents on Rural Roads Using Statistical and Artificial Neural Network Methods

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

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