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The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed. [ABSTRACT FROM AUTHOR]

Across the globe, landslides have been recognized as one of the most detrimental geological calamities, especially in hilly terrains. However, the correct determination of landslide-prone fields remained a challenging task due to the nonlinear, complex, and inconsistent nature of landslides. Therefore, it is essential to apply methods with superior capabilities in dealing with real-world problems for properly delineating potential landslide zones. Recently, ensemble learning techniques have been drawn intense interest in landslide susceptibility mapping studies due to their distinct advantages. This present work intended to propose natural gradient boosting (NGBoost), a novel member of the ensemble learning family, for modeling landslide susceptibility for Macka County of Trabzon province, Turkey. The predictive performance of NGBoost was compared to ensemble-based machine learning methods, namely random forest (RF) and XGBoost using five accuracy metrics including overall accuracy (OA), F1 score, Kappa coefficient, area under curve (AUC) value, and root-mean-square error to evaluate its competence and robustness. Besides, SHAP based on the game theory approach was implemented to interpret the influences of the predisposing factors on the produced model. Results indicated that the NGBoost method utilized for landslide susceptibility mapping problem for the first time had the greatest predictive ability (AUC = 0.898), followed by XGBoost (AUC = 0.871) and RF (AUC = 0.863), and outperformed the XGBoost and RF by approximately 6% in terms of OA. McNemar's statistical significance test results also confirmed the superiority of the proposed NGBoost method over the RF and XGBoost algorithms. [ABSTRACT FROM AUTHOR]

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The performances of the random forests, nearest neighbour, support vector machines, logistic regression, Naïve Bayes, and k-nearest neighbour algorithms, which are among the machine learning algorithms, were calculated and compared to predict the final exam grades of the students. The dataset consisted of the academic achievement grades of 1854 students who took the Turkish Language-I course in a state University in Turkey during the fall semester of 2019–2020. The results show that the proposed model achieved a classification accuracy of 70–75%. The predictions were made using only three types of parameters; midterm exam grades, Department data and Faculty data. Such data-driven studies are very important in terms of establishing a learning analysis framework in higher education and contributing to the decision-making processes. Finally, this study presents a contribution to the early prediction of students at high risk of failure and determines the most effective machine learning methods. [ABSTRACT FROM AUTHOR]

Technological advancements coupled with growing world population require the increasing need of energy. Natural gas is one of the most important usable energy resources. Turkey is with high external dependency on energy as it has its own limited natural and underground energy resources. Thus, in order to effectively and productively use of natural gas purchased from foreign countries and to make reliable and robust energy policies for the years ahead, it is crucial to make a reasonable and plausible prediction for natural gas consumption of Turkey. In this paper, we estimate the natural gas consumption using machine learning techniques on the basis of real monthly data representing natural gas consumption of Turkey between the years 2010 and 2018. The performances of machine learning techniques involving Artificial Neural Networks, Random Forest Tree, Regression, Time Series and Multiple Seasonality Time Series are compared in predicting the natural gas consumption of Turkey. Experimental results show that among the five techniques, artificial neural networks produce the best estimation, having the lowest mean square errors, followed by regression method. Time series shows the worst performance among all the techniques. [ABSTRACT FROM AUTHOR]

Cumulative absolute velocity (CAV) is a powerful intensity measure for quantifying potential earthquake damage to structures. Machine learning (ML) methods can provide more accurate and reliable predictions of cumulative absolute velocity due to handling nonlinear relationships, adaptability to changing conditions, automation, efficiency, and the potential for real-time predictions. This study aims to identify machine learning regressions with the highest accuracy for CAV prediction. Several supervised machine learning algorithms were applied and comprehensively compared for performance and accuracy in CAV prediction using the recently compiled Turkish strong-motion database. Support Vector Machine, Linear Regression, Random Forest, Artificial Neural Network, Bayesian Ridge Regression, and Gradient Boosting algorithms were evaluated and compared with traditional Ground Motion Models (GMMs). Two new datasets including 24,667 strong-motion recordings from Turkiye along with global strong-motion recordings are used to build machine learning models. The first dataset contains all recordings of events with 3.5 ≤ M o m e n t M a g n i t u d e (M w) ≤ 7.6 , while the second dataset contains only recordings with M w ≥ 5.5. Moreover, feature selection and outlier detection were performed as preprocessing steps to choose the best of seven CAV estimator parameters in order to boost the ML model performance. To measure the performance of ML methods five evaluation metrics were utilized which are mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), and correlation coefficient (R). Comparative assessment of the machine learning algorithms suggests that models trained by M w ≥ 5.5 dataset are quite successful in CAV prediction compared to predictive models trained by the 3.5 ≤ M w ≤ 7.6 dataset. The result proves that the Gradient Boosting models significantly outperform the other machine learning algorithms in terms of R and RMSE. Machine learning techniques are more successful with user-selected estimators representing the key components that make up earthquake recordings. Finally, the machine learning-based CAV prediction models for Turkiye are compared with available CAV GMMs, and it is observed that the machine learning-based models can predict CAV as successfully as GMMs if there are a sufficient number of recordings for training machine learning algorithms. • An extensive analysis of six machine learning (ML) algorithms for the prediction of cumulative absolute velocity (CAV). • An updated Turkish Strong Motion Database (>15000 recordings) was used to build the ML-based ground motion models. • ML-based models can predict CAV as successful as conventional ground motion prediction models. • Gradient Boosting is found to be superior to other methods in the prediction of CAV. [ABSTRACT FROM AUTHOR]

• Operational-performance data of RBMs collected from different raise boring projects in Turkey. • Indentation tests are performed on different core and block samples by using a real-life tungsten carbide insert. • Different indentation indices are suggested to predict the performance parameters of RBMs. • Indentation indices are correlated with linear cutting performance parameters obtained at optimum cutting conditions. • New empirical models including indentation indices are suggested for predicting performance of RBMs. Raise Boring Machines (RBMs) as the fast, safe, and efficient machines are used for drilling/excavation of vertical and/or inclined shafts in the underground structures. Proper selection and accurate performance prediction of these machines are very important issues affecting the cost estimation and planning of the projects. However, due to lack of reliable models, performance prediction of these machines has been seriously limited. This study aims at proposing new models for performance prediction of RBMs based on indentation and full-scale linear cutting tests, as well as physical–mechanical property tests, performed on rock samples. Operational and performance data of RBMs and rock samples with different geological origins are collected from different raise boring projects. Then, indentation tests are performed on different core samples by using a real-life tungsten carbide insert of the button cutters used on the analyzed RBMs. Full-scale linear cutting tests were previously performed on the block samples by using a 305 mm (12 in.) diameter button (kerf) cutter. The study indicates that velocity of S-wave and Cerchar abrasivity index can be used to estimate brittleness index of rocks, which is also obtained from the indentation tests. It also shows the feasibility of using power function as relationship between the indentation force and penetration values for performance prediction of RBMs. It is shown that the results of indentation tests can be used in both empirical and deterministic predictions of RBM performance. However, the proposed models should be improved by additional rock types, operational-performance parameters of different RBMs, indentation and cutting test results of different rocks. [ABSTRACT FROM AUTHOR]

• An explainable prediction of reservoir temperature using machine learning was proposed. • Natural gradient boosting proved superior over the remaining machine learning models. • SHAP interpretation revealed that SiO 2 was the most important variable. • An increase in SiO 2 concentration leads to increased temperature and vice versa The estimation of reservoir temperature is critical in geothermal exploration for the classification and utilisation of geothermal systems. A marginal increase in reservoir temperature predictive accuracy has the potential to significantly improve the efficiency of geothermal energy exploration and production. The goal of this study was to create five machine learning models to predict reservoir temperature using a hydrogeochemical dataset from Western Anatolia geothermal systems, in Turkey. The models developed include natural gradient boosting (NGB), extreme learning machine (ELM), group method of data handling (GMDH), generalised regression neural network (GRNN) and back propagation neural network (BPNN). The NGB model proved superior over the remaining models by obtaining the highest R2 of 0.9959 and the lowest RMSE and MAE values of 4.5938 and 3.9678, respectively. Further, this study utilised a model explainable technique known as Shapley additive explanation (SHAP) to evaluate why certain predictive decisions are reached by the NGB model in order to convey trust and to foster its adoption in real-time applications. By assessing the relationship between hydrogeochemical variables and reservoir temperature, the SHAP interpretation revealed that SiO 2 was the most important variable in predicting reservoir temperature. It was also found an increase in SiO 2 concentration leads to increased temperature and vice versa. The high predictive performance and the consistency between the SHAP interpretation and empirical knowledge indicate the reliability of the NGB model for estimating reservoir temperature. [ABSTRACT FROM AUTHOR]