A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth

Abstract

In this paper, a novel pareto evolutionary structure of adaptive neuro-fuzzy inference system (ANFIS) network is presented for abutment scour depth predicting. The genetic algorithm (GA) and singular value decomposition (SVD) is utilized in optimizing design of nonlinear antecedent parts and linear consequent parts of TSK-type of fuzzy rules simultaneously in ANFIS design for the first time. To this end, first the parameters affecting the scour in the vicinity of abutments are detected. After that, 11 ANFIS-GA/SVD models are introduced through the combination of the parameters affecting the scour. Based on the modeling results, the ANFIS-GA/SVD models predict the scour around abutments with a reasonable accuracy. The superior model forecasts more than 63% of scours with an error of less than 8%. The correlation coefficient (R) for the model is computed roughly 0.978. The value of the average discrepancy ratio for the model is obtained 0.981. In addition, the results of the sensitivity analysis demonstrate that the Froude number (Fr) and the ratio of the flow depth to the radius of the scour hole (h/L) are the most noticeable parameters affecting the scour depth in the vicinity of the abutments. Ultimately, a comparison between the superior model and the previous studies are presented which reveal that the current study has better performance to predict scour depth around abutments.

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

“…The optimized model [47] performed better than the classical model [46], as shown in Figure 6. The model of Azimi et al [47] provided excellent results in the training phase, but comparing the accuracy of this model with the ELM in the test phase, which indicated the generalizability of a model, demonstrated the superior performance of the developed ELM.…”

“…The optimized model [47] performed better than the classical model [46], as shown in Figure 6. The model of Azimi et al [47] provided excellent results in the training phase, but comparing the accuracy of this model with the ELM in the test phase, which indicated the generalizability of a model, demonstrated the superior performance of the developed ELM. Also, the developed method in the current study not only had a higher accuracy than existing ones but also eliminated the underlying problem of existing AI-based models [46,47] in providing a specific relationship for practical applications.…”

“…Consequently, the results indicated that neglecting the abutment geometry parameter in scour depth evaluation was significant, whereby ignoring it reduced d s /L performance and accuracy. To evaluate the proposed ELM model's accuracy in predicting the scour depth around abutments in clear water condition, the performance of the developed ELM-based model was compared with conventional regression-based models [22,23] and recently artificial intelligence (AI) based techniques [46,47] by combined accuracy (CA) index for training and testing phases. The lowest CA at both phases is related to Muzammil [23], who provided his model using conventional nonlinear repression.…”

“…However, there was a significant difference between the performance of regression methods and artificial intelligence methods. In addition to the developed ELM model, Moradi et al [46] and Azimi et al [47] models were also classified as AI-based models. Both of these methods were presented based on ANFIS so that the Moradi et al [46] was provided as classical ANFIS, while the model of Azimi et al [47] was introduced as a multi-objective evolutionary optimization of ANFIS.…”

“…The results indicated that the ANFIS-SC outperformed all other existing ANFIS techniques. Besides, Azimi et al [47] proposed a multi-objective design of ANFIS optimized by genetic algorithm. The authors indicated that this technique not only provided more accurate results but also enhanced the generalizability of the ANFIS model in abutment scour depth prediction.…”