TY - JOUR
T1 - Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines
AU - Sharafati, Ahmad
AU - Tafarojnoruz, Ali
AU - Motta, Davide
AU - Yaseen, Zaher Mundher
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization (ANFIS-PSO), ant colony (ANFIS-ACO), differential evolution (ANFIS-DE) and genetic algorithm (ANFIS-GA) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (θ), Keulegan–Carpenter number (KC) and embedded depth to diameter of pipe ratio (e=D) are considered as prediction variables. Results indicate that the ANFIS-PSO model (R 2 live bed ¼ 0:832 and R 2 clear water ¼ 0:984) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the ANFIS-PSO model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination (R-factor ave ¼ 4:3) than it is due to the model structure (R-factor ave ¼ 2:2).
AB - Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization (ANFIS-PSO), ant colony (ANFIS-ACO), differential evolution (ANFIS-DE) and genetic algorithm (ANFIS-GA) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (θ), Keulegan–Carpenter number (KC) and embedded depth to diameter of pipe ratio (e=D) are considered as prediction variables. Results indicate that the ANFIS-PSO model (R 2 live bed ¼ 0:832 and R 2 clear water ¼ 0:984) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the ANFIS-PSO model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination (R-factor ave ¼ 4:3) than it is due to the model structure (R-factor ave ¼ 2:2).
KW - Geotechnical Engineering and Engineering Geology
KW - Atmospheric Science
KW - Optimization methods
KW - Prediction
KW - Wave-induced scour
KW - Adaptive neuro-fuzzy inference system
KW - Uncertainty analysis
KW - Pipeline
UR - http://www.scopus.com/inward/record.url?scp=85092486640&partnerID=8YFLogxK
U2 - 10.2166/hydro.2020.184
DO - 10.2166/hydro.2020.184
M3 - Article
SN - 1464-7141
VL - 22
SP - 1425
EP - 1451
JO - Journal of Hydroinformatics
JF - Journal of Hydroinformatics
IS - 6
ER -