Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models

Abstract

Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs.

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

“…Soft computing (SC) methods, often indicated with the term artificial intelligence (AI), are increasingly being adopted to solve complex problems, due to lower cost of computation and higher flexibility and accuracy in comparison with physically based numerical models (Konar 2018;Yaseen et al 2019;Sharafati et al 2020;Tung and Yaseen 2020). SC models are capable of recognizing meaningful patterns in complex problems (Sharafati et al 2019a) and often adopt nature-inspired techniques (Barzegar et al 2016;Corchado and Aiken 2002;Konar 2018).…”

“…There are different types of SC models, other than ANN-based models, such as ANFIS and SVM, for estimating SST or other parameters (Awan and Bae 2016;Sharafati et al 2020). Fuzzy logic-based models originated from Zadeh (1965), who introduced the fuzzy logic (FL) rules to describe non-linear relations between inputs and outputs.…”

“…Jang (1993) presented a new FIS system implementing an automatic approach for parameter tuning, named Adaptive Neuro-Fuzzy Inference System (ANFIS): this model applies neural networks for tuning both designed and MF's parameters. To achieve this aim, ANFIS is linked to several heuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization (Sharafati et al 2020).…”

Modeling of wave run-up by applying integrated models of group method of data handling

“…Sharafati et al 24 confirmed the good accuracy of ANFIS-BBO in the prediction of long contraction scour depth. Sharafati et al 25 used teaching–learning-based optimization (TLBO), biogeography-based optimization (BBO), and invasive weed optimization (IWO) algorithms to optimize ANFIS parameters for the prediction of scour depth downstream of weirs. The results showed that ANFIS-IWO is a reliable technique for the prediction of scour depth.…”

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

“…Moreover, it has been used in the estimation of equilibrium bridge pier scour effectively [21,22]. Recently, Sharafati et al [23] predicted scour depth downstream of weirs by employing the adaptive neuro-fuzzy inference system (ANFIS) coupled with biogeography based optimization (ANFIS-BBO), invasive weed optimization (ANFIS-IWO) and teaching-learning based optimization (ANFIS-TLBO) algorithms. They found better performance with the ANFIS-IWO model (RMSE = 0.148) over the other models.…”