Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Abstract

Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes.

Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review

“…Grasshopper optimization algorithm [105] and cultural algorithm [106] are utilized to set parameters of ANFIS models. [114] Johnson [115] Shen [116] Laursen and Toch [13] Ratio of pier width to flow depth Ratio of pier length to flow depth [117] 104 One of the most important features of the studies is the characteristics of the data such as the quality, source, and properties. Details of the input data of the studies in Table 3 were noted here.…”

“…Moreover, the data set of [117] included both scour conditions of clear-water and live-bed. The effect of the independent parameters flow depth, velocity of approach, duration of flow, median sediment size was investigated on predicting scour.…”

“…Furthermore, [117] generated a model based on gradient tree boosting to predict scour depth around piers with different geometries: rectangular, circular, sharp-nosed, and round-nosed. In conclusion, gradient tree boosting was more accurate and effective than the group method of data handling for all shapes.…”

Novel Approaches for Structural Health Monitoring II

“…Grasshopper optimization algorithm [105] and cultural algorithm [106] are utilized to set parameters of ANFIS models. [114] Johnson [115] Shen [116] Laursen and Toch [13] Ratio of pier width to flow depth Ratio of pier length to flow depth [117] 104 One of the most important features of the studies is the characteristics of the data such as the quality, source, and properties. Details of the input data of the studies in Table 3 were noted here.…”

Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning

“…A simple model is then fitted to each region. Sreedhara et al [53] describe the method used in this study in detail. The features used in this method were batch size 100, classifier = REPTree, max depth = 0, number of executions slots = 1, number of iterations = 10, and random seed = 1.…”