Predicting scour depth in a meandering channel with spur dike: A comparative analysis of machine learning techniques DOI
Zeeshan Akbar,

Nadir Murtaza,

Ghufran Ahmed Pasha

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(4)

Published: April 1, 2025

In this research, an assessment of scour depth prediction in meandering channels with spur dikes is made employing machine learning approaches. Efficient determination the therefore vital morphologic aspects and structural stability. The input parameters include sinuosity (S), dike locations (Ld), porosity (P) experimental data from sinusoidal flumes. Four models; Extreme Gradient Boosting (XGBoost) Particle Swarm Optimization (PSO) XGBoost-PSO, Random Forest (RF), k-Nearest Neighbors (k-NN), Decision Tree-Neural Network (DT-NN) were used compared. findings demonstrate R-value 0.995 case RF model while XGBoost-PSO gave second-best accuracy R = 0.988. results SHAP analysis illustrated that are significant factors affecting (Ds/Yn, Ds: depth, Yn: water depth) had moderate importance assigned to location. Kernel density plots further supported regarding error distribution consistency. Even though, both yielded better because hyperparameter tuning, k-NN DT-NN less precise outcomes specifically predicted for progressive hydraulic procedures. Taylor's diagram even revealed greater by RF. Hence, a proper selection appropriate models remains first step estimating sufficiently flood erosion control.

Language: Английский

A Sediment Process Simulation on the Steep Area of the Upper Yangtze River Basin Using a Hybrid Distributed Soil Erosion Model DOI Open Access
Yibo Wang,

Ye Jin,

Hongwei Bi

et al.

Water, Journal Year: 2025, Volume and Issue: 17(7), P. 996 - 996

Published: March 28, 2025

Accurate simulation and forecast for soil processes has always been a challenge river management environmental conservation. However, the sediment modeling technique remains insufficient catchments characterized by special erosion conditions, especially steep area of upper Yangtze River basin. This study presents framework that incorporates transport calculation modules into distributed hydrological model, customized modifications are applied to fit catchment conditions. In addition, accurately describe topography (e.g., slope length steepness) account its impact on process simulation, sub-basin with high yield is discretized higher spatial resolution. The presented validated in Heishuihe basin southwestern China. And results show modified version DDRM model (i.e., DDRM-SED) good performance terms flow processes. DDRM-SED multi-spatial resolution better than constant

Language: Английский

Citations

0
Predicting scour depth in a meandering channel with spur dike: A comparative analysis of machine learning techniques DOI
Zeeshan Akbar,

Nadir Murtaza,

Ghufran Ahmed Pasha

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(4)

Published: April 1, 2025

In this research, an assessment of scour depth prediction in meandering channels with spur dikes is made employing machine learning approaches. Efficient determination the therefore vital morphologic aspects and structural stability. The input parameters include sinuosity (S), dike locations (Ld), porosity (P) experimental data from sinusoidal flumes. Four models; Extreme Gradient Boosting (XGBoost) Particle Swarm Optimization (PSO) XGBoost-PSO, Random Forest (RF), k-Nearest Neighbors (k-NN), Decision Tree-Neural Network (DT-NN) were used compared. findings demonstrate R-value 0.995 case RF model while XGBoost-PSO gave second-best accuracy R = 0.988. results SHAP analysis illustrated that are significant factors affecting (Ds/Yn, Ds: depth, Yn: water depth) had moderate importance assigned to location. Kernel density plots further supported regarding error distribution consistency. Even though, both yielded better because hyperparameter tuning, k-NN DT-NN less precise outcomes specifically predicted for progressive hydraulic procedures. Taylor's diagram even revealed greater by RF. Hence, a proper selection appropriate models remains first step estimating sufficiently flood erosion control.

Language: Английский

Citations

0