Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

Bonakdari, Hossein; Ebtehaj, Isa; Gharabaghi, Bahram; Sharifi, Alimorad; Mosavi, Amir

doi:10.20944/preprints202001.0313.v1

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…The initial step involved loading the data and categorizing it into two main groups: the training and testing datasets. The widely recognized random selection without replacement (RSWR) approach [25,26] commonly used in machine learning modeling was employed to accomplish this. Through RSWR, 30% of the total samples were selected for the testing stage, while the remaining samples were utilized for training the model.…”

Section: Hybrid Methodologymentioning

confidence: 99%

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Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

et al. 2023

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In the current study, a new hybrid machine learning (ML)-based model was developed by integrating a convolution neural network (CNN) with a random forest (RF) to forecast pesticide use on golf courses in Québec, Canada. Three main groups of independent variables were used to estimate pesticide use on golf courses, expressed as actual active ingredient rate (AAIR): (i) coordinates (i.e., longitude and latitude of the golf course), (ii) characteristics of the golf courses (i.e., pesticide type and the number of holes), and (iii) meteorological variables (i.e., total precipitation, P, and average temperature, T). The meteorological variables were collected from the Google Earth Engine by developing a JavaScript-based Code. On the basis of the different periods of total precipitation and average temperature, four different scenarios were defined. A data bank with more than 40,000 samples was used to calibrate and validate the developed model such that 70% of all samples were randomly selected to calibrate the model, while the remainder of the samples (i.e., 30%) that did not have any role in calibration were employed to validate the model’s generalizability. A comparison of different scenarios indicated that the model that considered the longitude and latitude of the golf course, pesticide type, and the number of holes in golf courses as well as total precipitation and average temperature from May to November as inputs (R = 0.997; NSE = 0.997; RMSE = 0.046; MAE = 0.026; NRMSE = 0.454; and PBIAS (%) = −0.443) outperformed the other models. Moreover, the sensitivity analysis result indicated that the total precipitation was the most critical variable in AAIR forecasting, while the average temperature, pesticide types, and the number of holes were ranked second to fourth, respectively.

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Section: Hybrid Methodologymentioning

confidence: 99%

“…random selection without replacement (RSWR) approach [25,26] commonly used in machine learning modeling was employed to accomplish this. Through RSWR, 30% of the total samples were selected for the testing stage, while the remaining samples were utilized for training the model.…”

Section: Hybrid Methodologymentioning

confidence: 99%

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

et al. 2023

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“…In other words, the discharge capacity of the asymmetrical configuration is improved by 50% compared to the symmetric weir. Bonakdari et al (2020) estimated the discharge capacity of the labyrinth weir using gene expression programming (GEP). Using the ratio of crest height to the hydraulic head over the weir (w/y or P/y), the ratio of crest length to channel width (L/W), the ratio of crest length to hydraulic head (L/y), Froude number (Fr), and vortex angle (θ), they presented a numerical relation by the GEP method (Eq.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of the discharge coefficient of labyrinth weirs with asymmetric cycles

Elahifar

Tayari

Yazdanpanah

et al. 2022

WSA

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This study investigated the discharge coefficient in asymmetric rectangular labyrinth weirs. A dimensional analysis was carried out which showed that the discharge coefficient is a function of dimensionless parameters, such as the ratio of asymmetric widths of left and right cycles (wL/wR), the ratio of the total hydraulic head to the weir height (Ht/P), and the weir length ratio (B/wavg). The experimental results for the discharge coefficient were found to decline as wL/wR increased or B/wavg decreased. For wL/wR = 1.19, the ratio B/wavg = 2.76 improves the discharge coefficient by nearly 12.7% compared to B/wavg = 3.1. For wL/wR = 1.42, the ratio B/wavg = 2.76 improves the discharge coefficient by nearly 34.2% compared to B/wavg = 3.1. For wL/wR = 1.70, the ratio B/wavg = 2.76 improves the discharge coefficient by nearly 30% compared to B/wavg = 3.1.

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Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirs

et al. 2022

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Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

Cited by 6 publications

References 27 publications

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques

Experimental investigation of the discharge coefficient of labyrinth weirs with asymmetric cycles

Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirs

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