TY - JOUR ID - 21738 TI - Bridge backwater estimation: A comparison between artificial intelligence models and explicit equations JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Niazkar, M. AU - Talebbeydokhti, N. AU - Afzali, S. H. AD - School of Engineering, Department of Civil and Environmental Engineering, Shiraz University, Zand Blvd., Shiraz, Iran AD - School of Engineering, Department of Civil and Environmental Engineering, Shiraz University, Zand Blvd., Shiraz, Iran. Y1 - 2021 PY - 2021 VL - 28 IS - 2 SP - 573 EP - 585 KW - Hydraulic structures KW - bridge backwater estimation KW - Genetic programming KW - explicit equation KW - artificial neural network DO - 10.24200/sci.2020.51432.2175 N2 - Estimation of bridge backwater has been one of practical challenges in hydraulic engineering for decades. In this study, Genetic Programming (GP) has been applied for estimating bridge backwater for the first time based on the conducted literature review. Furthermore, two new explicit equations are developed for predicting bridge afflux using Genetic Algorithm (GA) and hybrid MHBMO-GRG algorithm. The performances of these models are compared with Artificial Neural Network (ANN) and several explicit equations available in the literature considering both laboratory and field data. Based on five considered performance evaluation criteria, the two new explicit equations outperform the ones available in the literature. Furthermore, GP and ANN achieve the best results in favor of four out of five considered criteria for train and test data, respectively. To be more specific, ANN improves the MSE and R2 values of the explicit equation developed using GA by 44% and 12% for the test data while GP enhances the corresponding values by 62% and 9% for the train data. Finally, the results demonstrate that not only artificial intelligence models considerably improve bridge afflux estimation than the explicit equations but also the suggested equations significantly improve the accuracy of the available explicit ones. UR - https://scientiairanica.sharif.edu/article_21738.html L1 - https://scientiairanica.sharif.edu/article_21738_e2c06ad90c2d537acd956ffe65ff1d46.pdf ER -