Open Channel Sluice Gate Scouring Parameters Prediction: Different Scenarios of Dimensional and Non-Dimensional Input Parameters

Ali Yousif,Mohammad Ehteram,Shamsuddin Shahid,Nadhir Al-Ansari,Lamine Diop,Sadeq Sulaiman,Zaher Yaseen

doi:10.3390/w11020353

Abstract

The determination of scour characteristics in the downstream of sluice gate is highly important for designing and protection of hydraulic structure. The applicability of modern data-intelligence technique known as extreme learning machine (ELM) to simulate scour characteristics has been examined in this study. Three major characteristics of scour hole in the downstream of a sluice gate, namely the length of scour hole (Ls), the maximum scour depth (Ds), and the position of maximum scour depth (Lsm), are modeled using different properties of the flow and bed material. The obtained results using ELM were compared with multivariate adaptive regression spline (MARS). The dimensional analysis technique was used to reduce the number of input variable to a smaller number of dimensionless groups and both the dimensional and non-dimensional variables were used to model the scour characteristics. The prediction performances of the developed models were examined using several statistical metrics. The results revealed that ELM can predict scour properties with much higher accuracy compared to MARS. The errors in prediction can be reduced in the range of 79%–81% using ELM models compared to MARS models. Better performance of the models was observed when dimensional variables were used as input. The result indicates that the use of ELM with non-dimensional data can provide high accuracy in modeling complex hydrological problems.

Highlights

Sluice gates are widely used in rivers, dams, spillways and barrages to control floods, retention of water and maintaining optimum flow [1,2]
The extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) models were developed in this study to predict three parameters of sluice gate scour (Y) namely, the length of scour hole (Ls), the maximum depth of scour (Ds), and the position of maximum scour depth (Lsm) using different combinations of the predictors (X) which include the diameter of bed material, gate opening, velocity of flow under the gate, effective head of flow and the density of water and bed material
The ELM and MARS were used to develop regression models based on physical relationship between the predictors and the predictand, 80% of experimental dataset were used for the training of the models and the remaining 20% of the dataset were used for model validation

Summary

Introduction

Sluice gates are widely used in rivers, dams, spillways and barrages to control floods, retention of water and maintaining optimum flow [1,2]. One of the earliest studies conducted on scouring parameters determination using soft computing models by Reference [13], where a fuzzy model was used for the estimation of the scour parameters downstream of a dam's vertical gate and soft computing methods were found to be. Najafzadeh and Lim [26] developed an intelligent model based on the integration neuro-fuzzy, group method of data handling used particle swarm optimization (NF-GMDH-PSO) to estimate the local scour in the downstream of a sluice gate with an apron. Two recently developed soft computing techniques known as extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) were adopted to estimate the three major characteristics of the geometry of scour hole, namely maximum scour depth, position of maximum scour depth and length of scour hole in the downstream of sluice gate. The accuracy and the precision of ELM models were compared with MARS models to show the efficacy of ELM in modeling scouring phenomena

Laboratory Experiment of Scouring in Sluice Gate

Description of the Models

Multivariate Adaptive Regression Spline

Model Development

Results and Discussion

Conclusions