Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi, Alireza; Ebrahimabadi, Arash

doi:10.1007/s42452-020-03767-y

Cited by 22 publications

(5 citation statements)

References 84 publications

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“…They reported that the ANN and SVM techniques produced more accurate models than the MLR technique. A slightly higher accuracy of the ANN model (R = 0.99) when compared to SVM (R = 0.97) was reported by Afradi and Ebrahimabadi [47] who used AI methods to predict the penetration rate of tunnel boring machine. Sabzi-Nojadeh et al [48] compared the accuracy of ANN and MLR models used to predict the oil yield and trans-anethole yield of fennel populations; ANN performed better (R = 0.96 and R = 0.88) than MLR (R = 0.74 and R = 0.68).…”

Section: Discussionmentioning

confidence: 80%

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Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

et al. 2022

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The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry fruit varieties relating to harvest time, water content, as well as storage duration and conditions. After 25 days in storage, the fruit of the investigated varieties were found with a decrease in mean acidity, from 1.56 g·100 g−1 to 1.42 g·100 g−1, and mean water content, from 89.71% to 87.95%. The findings showed a decrease in breaking energy; there was also a change in the apparent modulus of elasticity, its mean value in the fresh fruit was 0.431 ± 0.07 MPa, and after 25 days of storage it decreased to 0.271 ± 0.08 MPa. The relationships between the cranberry varieties, storage temperature, duration of storage, x, y, and z dimensions of the fruits, and their selected mechanical parameters were modeled with the use of multiple linear regression, artificial neural networks, and support vector machines. Machine learning techniques outperformed multiple linear regression.

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Section: Discussionmentioning

confidence: 80%

“…− deformation at the breaking point (λ) 7.83 ± 1. 47 Statistical data are expressed as means ± SD. Means in a column followed by different letters show significant differences (α = 0.05) according to the LSD test.…”

Section: Mechanical Properties Of Cranberry Fruitmentioning

confidence: 99%

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

et al. 2022

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“…The Support Vector Machine (SVM) is a widely adopted classifier that achieves sample classification by identifying separating hyperplanes. In this experiment, SVM serves as the foundation for training the classification of wool fiber texture features [10]. Moreover, to address the intricacies of sample classification, a kernel function is introduced during the actual classification process.…”

Section: Intelligent Recognition Of Wool Fibersmentioning

confidence: 99%

Intelligent Identification of Chinese and Australian Merino Wool Fibers Based on Image Recognition

Wang,

Chen,

Wang

et al. 2024

KEM

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In order to promote the sustainable growth of the wool industry and protect consumers' legitimate rights, rapid identification of the country of origin for wool of the same type is deemed crucial. This research presents a computer graphic recognition training model that utilizes median and Wiener filtering techniques to effectively reduce noise in the raw wool fiber images. Employing a support vector machine as the classifier and integrating a polynomial kernel function, this model achieves swift and accurate identification of Chinese and Australian Merino wool fibers. Experimental results underscore that following image recognition training, the model attains an impressive 92.5% comprehensive and precise identification rate for Chinese and Australian Merino wool fibers, effectively distinguishing the origin of wool from the same category. This approach not only provides a valuable reference for identifying the origin of similar wool types but also holds the potential to standardizing the wool fibre material market and assuring the consumer’s confidence on wool products.

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“…Over time, for optimizing the nonlinear problems, the ML models, including the support vector machine (SVM), have been utilized in numerous fields such as for predicting the penetration rate of tunnel-boring machines [33], solar radiation prediction [34], streamflow forecasting [35], landslide hazard modelling [36][37][38], seawater level simulation [39], forecasting electric load [40], and infiltration simulation [41,42]. The SVM approach was recommended by Vapnik [43] and derived from statistical learning theory to solve classification and regression problems [44].…”

Section: Support Vector Machinementioning

confidence: 99%

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

et al. 2022

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Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations.

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Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Cited by 22 publications

References 84 publications

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

Intelligent Identification of Chinese and Australian Merino Wool Fibers Based on Image Recognition

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

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