رویکردهای نوین در مهندسی آب و محیط‌زیست
نشریه علمی

Estimation of Discharge Capacity in Rectangular Flumes Containing Side Semi-Cylinders Using Data-Driven Soft Computing Models

نوع مقاله : مقاله پژوهشی

نویسندگان

عباس پارسائی 1
محمد بهرامی یاراحمدی 2
فرید احمدی 1

1 Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Associate Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

10.22034/nawee.2026.574888.1194
چکیده
This study investigates the discharge capacity of Semi-cylindrical contracted flumes installed in rectangular channels under both free-flow and submerged regimes. Two data-driven models, namely the Multilayer Perceptron Neural Network (MLPNN) and the Group Method of Data Handling (GMDH), are developed and compared. After dimensional analysis, influential dimensionless parameters were identified and used as model inputs. The MLPNN model with a single hidden layer of five neurons and tangent sigmoid activation function achieved outstanding accuracy for both free-flow (R² = 0.987, RMSE = 0.001) and submerged conditions (R² = 0.992, RMSE = 0.001). The GMDH model, while slightly less accurate (R² = 0.88 for free-flow and 0.96 for submerged), offered superior interpretability due to its transparent, self-organizing structure. Sensitivity analysis revealed that the depth-to-throat ratio (h/w) is the most influential parameter in free-flow, whereas the contraction ratio (w/B) dominates in submerged conditions. The MLPNN model is recommended for high-precision applications, whereas GMDH serves as a robust alternative when model interpretability is prioritized.

کلیدواژه‌ها

SMBF flume
Soft Computing
Artificial Neural Network (MLPNN)
Group Method of Data Handling (GMDH)
Free and Submerged Flow
Sensitivity Analysis
رویکردهای نوین در مهندسی آب و محیط‌زیست
دوره 5، شماره 4
زمستان 1405
صفحه 1-18

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