Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

Norouzi, Reza; Sihag, Parveen; Daneshfaraz, Rasoul; Abraham, John; Hasannia, Vadoud

doi:10.2166/ws.2021.193

Cited by 9 publications

(1 citation statement)

References 31 publications

(45 reference statements)

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“…Sihag et al, (2019) used ANFIS, SVM and random forest (RF) for the prediction of cumulative infiltration (CI) and infiltration rate (IR) in arid areas in Iran. Norouzi et al, (2021) applied ANN, ANFIS, generalized neural network (GRNN), SVM, GP, LR, and multiple linear regression MLR to predict the relative energy dissipation for vertical drops equipped with a horizontal screen and found that the performance of ANFIS model was outperforming among other applied models. The performance of SVM based model was better than ANFIS and RF based model for the prediction of CI and IR.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Undrained Shear Strength of Crushed Tire Mixture with Fine-Grained Soil by using Machine Learning Approaches

Zarei

Sihag

Rahimi

2021

Preprint

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This research examines the ability of soft computing approaches (i.e. Linear Regression (LR), Gaussian Process regression (GP), Adaptive neuro-fuzzy inference system (ANFIS),Support Vector Machine (SVM) and deep neural network (DNN)) to predict the undrained shear strength (SU) of soil mixed waste crushed tires. Data set consisting of 72 different samples were used and obtained from the laboratory experiments. Out of 72 experimental observations randomly separated 50 observations were selected for model development whereas residual 22 were selected for the validation of the developed models. Input data set consist of vertical stress, percentage of the crushed tire, percentage of clay, size of clay, specific gravity of tires, Liquid limit, Plastic limit and Specific gravity of clay samples were considered as inputs whereas undrained shear strength of stabilized soil using waste crushed tires material (SU) was considered as output. Five most popular goodness fit assessment parameters were used for the comparison among developed models. Results suggest that DNN based model works superior to other developed models for the prediction of SU the soil samples mixed with tires waste material with coefficient of correlation values as 0.9975, 0.9736, Root mean square error values as 2.4198, 7.5319, Mean absolute error values as 1.8407, 6.1870, Scatter index values as 0.0311, 0.0959 and Nash Sutcliffe model efficiency values as 0.9943, 0.9387 for training and testing stage respectively. Sensitivity analyses offer that specific gravity of tires, size of clay and vertical stress were the most influencing variables in the prediction SU the soil samples mixed with tires waste material.

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Section: Introductionmentioning

confidence: 99%