@article {10.3844/ajeassp.2017.959.964,
article_type = {journal},
title = {Estimating the Scouring Depth of Bridge Pier Using Self-Organizing Neural Networks (SOM)},
author = {Rafat, Abolfazl and Barani, Gholam Abbas and Naseri, Amineh},
volume = {10},
number = {4},
year = {2017},
month = {Nov},
pages = {959-964},
doi = {10.3844/ajeassp.2017.959.964},
url = {https://thescipub.com/abstract/ajeassp.2017.959.964},
abstract = {Scouring is caused as a result of erosion of river bed by water flow and materials carried by water. This research estimates the scouring depth using self-organizing neural network (SOM). The obtained findings were compared with findings of other models. It was found that self- organizing neural network (SOM) has higher correlation coefficient (0.98), compared to other methods. It was also found that root mean square error (RMSE = 0.112) is less than other methods. Estimating the depth of scouring using self-organizing neural network (SOM) method indicated that this method gives better findings, in a way that correlation coefficient in implementing the program with dimensional data is higher value compared to state in which program is implemented with non-dimensional data. In addition, Root Mean Square Error (RSME = 0.09) was seen less in the state of dimensional data. In the current research, using the sensitivity analysis showed that when SOM program is implemented with dimensional data, it will be more sensitive to parameter of average diameter of particles.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}