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Inaccurate cost estimates significantly affect the ultimate cost of construction projects and reduce revenues. This study compares the accuracy of two algorithm models in estimating construction project prices during the early conceptual stage of project development. The construction prices of residential projects are forecasted in this study using two modeling methods, artificial neural networks (ANN) and random forest (RF), based on the project data from the actual projects. The datasets have been collected from 220 residential buildings implemented in various cities in Egypt. Python was used to build the model during the training and testing phases. To evaluate the performance of the models generated by each algorithm, five key statistical metrics were employed. The findings revealed that both ANN and RF managed to predict the price per meter square of the construction projects, attaining model score accuracies of 96.6% and 93.05%, respectively. The best training for RF was acquired with 110 trees and a maximum decision tree depth of 20. The outcomes of this research could assist stakeholders in the building sector estimate construction project costs during the conceptual stage, enabling more informed decisions by examining key cost drivers for building projects and utilizing appropriate machine learning techniques for analysis. [ABSTRACT FROM AUTHOR]

• Integrated CPFEM-ML method is developed for defect induced fatigue life prediction. • Data augmentation with FIPs is used to predict fatigue life with different R ratios. • CNN achieves high accuracy in fatigue life prediction for microstructural features. Fatigue life prediction based on the traditional empirical formulation in large fatigue regimes is limited and prone to over-fitting. In this regard, we propose an integrated approach for fatigue life prediction of lamellar titanium alloys incorporating crystal plasticity simulations and a variety of machine learning methods from a microstructural perspective. Firstly, various defect shapes and distinct lamellar distributions are employed to characterize fatigue performance under different microstructural features. By utilizing fatigue indicator parameters (FIPs) to get an expanded dataset, this work employs algorithms including artificial neural networks (ANN), random forests regression (RFR), support vector regression (SVR), and particularly convolutional neural networks (CNN) to predict fatigue life. The results indicate that CNN outperforms the previously mentioned algorithms in prediction accuracy, due to its ability to directly extract microstructural features from images. Our proposed approach provides a microstructural perspective for fatigue life prediction of lamellar titanium alloys, broadening the way for life assessment of materials with diverse microstructures. [ABSTRACT FROM AUTHOR]

Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence. • Apply machine learning models to non-recurrent congestion caused by disaster. • Explore their ability to (1) predict traffic states and (2) interpret the results. • XGBoost method produced the best results in terms of prediction accuracy. • Deep neural network models are better in terms of the interpretability. • Confirm the best prediction accuracy is not always the best for practical use. [ABSTRACT FROM AUTHOR]

An experimental study is conducted to solve a binary classification problem, and to predict the country of residence (India and Hungary) for the students towards Information Communication and Mobile Technology with various parameter. Four supervised machine learning algorithms such as Random Forest (RF), K-nearest Neighbours (KNN), Multilayer Perceptron (ANN) and Support Vector Machine (SMO) are modeled in Weka tool on primary dataset (331 instances & 46 attributes) with the four parameters Attitude (A), Development and Availability (DA), Usability (U) and Educational Benefit (EB) with Feature Selection. Initially, these algorithms are applied on individual datasets and later, these are applied on Aggregate (AG) dataset. The datasets are trained and tested using three different type of testing methods such as Leave One Out, Hold Out and K-Fold Cross Validation (CV). Therefore, a statistical T-test at 0.05 significance level is also used to compare the prediction accuracy of classifiers on individual datasets and accuracy are measured. Afterward, Feature Selection methods such as ClassifierSubsetEval (CSE), GainRatioAttribu-teEval (GRAE) and InfoGainAttributeEval (IGAE) are also applied to enhance the prediction accuracy of AG-37 dataset. Initially, with T-test, the RF classifier with the Leave One Out testing method outperformed others. It is also explored that the CSE filter significantly improved the prediction accuracy of KNN, ANN, and SMO using filtered out 8, 14 and 15 features respectively. Further, 10 ranked features by GRAE filter significantly enhanced the accuracy of CSE filter for the KNN classifier and also for the RF classifier on AG-37. Latter, IGAE filter also boosted the prediction accuracy of the KNN, SMO and RF with 11 significant features. Also, the highest prediction accuracy 91% is gained by SMO with CSE-15 features, 90.3% accuracy is gained by RF with GRAE-10 features and 92.8% accuracy is gained by RF with IGAE-11. Finally, to predict the country of residence of the students towards Information Communication and Mobile Technology, the RF classifier outperformed others using Feature Selection in optimum time (0.14 seconds). [ABSTRACT FROM AUTHOR]

Accurate load forecasting is important to mitigate the negative impact of Electric vehicle integration into the existing grid. Previous studies mostly focus on individual or aggregated levels without specifying the impact of accuracy due to the selection of different spatial levels and lack the integration of uncertainty estimation in the forecasting models. To address these issues, this study compares the predictive performance of a Random Forest and Artificial Neural Networks at different spatial levels with 15-min resolution data across case studies (i) with 2 Electric Vehicles charging poles and 3 users, (ii) with 75 charging poles, 8 charging rails and 70 users. The outcome shows that forecasting the Electric Vehicle load of smaller case studies will require the presence or calendar information of users. Whereas in case studies with more than 10 charging piles, the features "previous week's power", "hour of the day" and the "number of connections" can achieve similar results. The results also showed that the aggregated forecasting was more accurate than individual charging piles. Moreover, the uncertainty plot generated for a 90% prediction interval showed that the uncertainty estimates were more reliable for the case study with large numbers of Electric Vehicles. • Framework for Electric Vehicle load forecasting with spatial considerations. • Integration of uncertainty estimation using quantile regression. • Comparative study between Random Forest and Artificial Neural Networks models. • Aggregated Electric Vehicle load forecasting outperforms individual charging piles. [ABSTRACT FROM AUTHOR]

This work contributes to this ongoing debate on the nature and the characteristics of propagation channels of crash events in international stock markets. Specifically, we investigate transmission mechanisms across stock markets along with effects from bond and currency markets. Our approach comprises a solid forecasting mechanism of the probability of a stock market crash event in various time frames. The developed approach combines different machine learning algorithms which are presented with daily stock, bond and currency data from 39 countries that cover a large spectrum of economies. Specifically, we leverage the merits of a series of techniques including Classification Trees, Support Vector Machines, Random Forests, Neural Networks, Extreme Gradient Boosting, and Deep Neural Networks. To the best of our knowledge, this is the first time that Deep Learning and Boosting approaches are considered in the literature as a means of predicting stock market crisis episodes. The independent variables included in our data contain information regarding both the two fundamental linkage channels through which financial contagion can be initiated: returns and volatility. We apply a suite of machine learning algorithms for selecting the most relevant variables out of a large set of proposed ones. Finally, we employ bootstrap sampling for adjusting the imbalanced nature of the available fitting dataset. Our experimental results provide strong evidence that stock market crises tend to exhibit persistence. We also find significant evidence of interdependence and cross-contagion effects among stock, bond and currency markets. Finally, we show that the use of Deep Neural Networks significantly increases the classification accuracy, while offering a robust way to create a global systemic early warning tool that is more efficient and risk-sensitive than the currently established ones. Thus, central banks may use these tools to early adjust their monetary policy, so as to ensure financial stability. [ABSTRACT FROM AUTHOR]

Highlights • Machine learning models are compared for simulation and forecasting of streamflows. • Support Vector Regression, Artificial Neural Networks and Random Forest are used. • A base flow separation method is coupled with machine learning models. • North Fork, Chehalis, Carson and Sacramento rivers in the United States are analysed. • Base flow separation method improved streamflow simulation to a certain degree. Abstract Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing to the high number of interrelated hydrological processes. It is well-known that machine learning models could fail in simulating streamflows from only meteorological variables in the absence of antecedent streamflow values. The main reason for this could be low and lagged relationships between streamflow and meteorological variables. To overcome this inefficiency, for the first time, we developed a simulation framework by coupling a base flow separation method to three machine learning methods. It was demonstrated that separating streamflow into different components such as base flow and surface flow can be useful for improving simulation and forecasting capabilities of machine learning models. We simulated streamflow in four rivers in the United States with Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Random Forest (RF) as a function of precipitation (P), temperature (T) and potential evapotranspiration (PET). We concluded that the base flow separation method improved the simulation performances of the machine learning models to a certain degree. Apart from the simulation scheme, we also employed a forecasting scheme by using the antecedent observed discharge values in addition to P, T, and PET. We discussed performances of models in simulation and forecasting of streamflow regarding model types, input structures and catchment dynamics in detail. [ABSTRACT FROM AUTHOR]

Main aspect of this study is to explore the possible use of Machine learning based approaches in the petroleum industry. The fourth industrial revolution is being ushered in by advances in computer power. The oil and gas sector contains a huge amount of produced oil data from wells that can be utilised to create data-driven models which can validate empirical correlations. This validation is vital in the current context because the oil market fluctuates a lot, thus making efficient judgments based on insights supplied by data science and machine learning algorithms are valuable. Artificial Neural Network (ANN), Random Forest Regressor (RF) and Gradient Boosting Regressor (GB) were used in this study to forecast daily oil production using available production parameters. In this study, the production data from Equinor's Volve Field was utilised. The obtained data was filtered, and then it was applied to mentioned algorithms with fine tuned hyperparameters. The data was split into training data (70%) and test data (30%). The model was constructed on training data then it was validated through test data. Seven parameters were chosen on the basis of magnitude of correlation coefficient. The result showed that even with the cases of several shut-ins, the hydrocarbon forecasting method for well 159-F-1C predicted production rates with a coefficient of determination (R2score) value greater or equal to 0.90. For performance analysis, coefficient of determination (R2score), mean squared error (MSE) and mean absolute error (MAE) were used for the selected wells. The outcome showed that prediction of hydrocarbon production for well 15/9-F-1 C ANN model showed the best performance (R2 score = 0.9, MAE = 20.52 and MSE = 1268) and for well 15/9-F-12 RF model shows the best performance (R2 score = 0.98, MAE = 84.95 and MSE = 37356.07). This novel approach could be trained on any dataset to assist in oil forecasting. • If sufficient historical data of any field is available it is possible to construct a model that can predict hydrocarbon production. • Data-driven approaches can predict hydrocarbon production for unconventional resources. • Machine learning algorithms cannot replace the conventional methods, but can assist conventional methods in improving efficiency and accuracy. • Different machine learning algorithms should be trained and evaluated on the dataset for selecting the best algorithm. • There is no one specific model for production prediction as no machine learning algorithm is superior. [ABSTRACT FROM AUTHOR]

Due to the significant delay and cost associated with experimental tests, a model-based evaluation of concrete compressive strength is of high value, both for the purpose of strength prediction as well as the mixture optimization. In contrast to the prior recent studies employing a single regression model, in this paper, we present a combined multi-model framework where the regression methods based on artificial neural network, random forest regression and polynomial regression are jointly implemented for compressive strength prediction with a higher accuracy. The outcomes of the individual regression models are combined via a linear weighting strategy and optimized over the training data set as a quadratic convex optimization problem. It is worth mentioning that due to the convexity of the formulated problem, the globally optimum weighting strategy is obtained via standard numerical solvers. Afterwards, employing the obtained regression model, a multi-objective genetic algorithm-based method is proposed for mixture optimization under practical constraints, where a Pareto front of the cost-CS trade-off has been obtained employing the available data set. Numerical evaluations show that the proposed multi-model regression achieves a significantly higher prediction accuracy, i.e., approximately 18% reduction in the obtained prediction mean squared error, without weight optimization, and roughly 30% reduction in the obtained prediction mean squared error with an optimized combination following a convex quadratic optimization, compared to the best single model regression method employing a multi-layered artificial neural network. [Display omitted] • Proposing a data-aided regression method for concrete compressive strength prediction. • Employing a combined multi-model approach to improve prediction accuracy. • Mixture parameter optimization via NSGA-II, employing the obtained regression model. [ABSTRACT FROM AUTHOR]

The forecasting of river ice jams faces challenges relating to both the availability of data and the complexity of river ice dynamics, resulting in difficulties in model formulation. In this study, a hybrid ensemble modelling framework is developed to address the data scarcity issue and leverage advanced machine learning techniques for the prediction of ice jams with a one-day lead time. The proposed methodology utilises data easily monitored in advance of any ice jam events and maintains a realistic balance between ice jam and non-ice jam events. A combination of both single model algorithms, including classification trees, logistic regression, k -nearest neighbors, support vector machines, and artificial neural networks, and ensemble model algorithms, including random forest, adaptive boosting, gradient boosting, variance penalizing adaptive boosting, logistic boosting, class switching, and adaptive resampling and combining, are considered for both the member models of the first layer of the hybrid ensemble and for the ensemble combiner of the second layer. The final selection of both variables and member models for the hybrid ensemble is detailed, with a focus on the reduction of false negatives, the prediction of no ice jam on a day when one occurs. The proposed method is applied to the St. John River in New Brunswick, Canada, in a location particularly prone to ice jam flooding. Using the proposed methodology, a final model combining 6 different member models using a support vector machine as the ensemble combiner was produced, with a balanced prediction accuracy of 86%. This hybrid ensemble model outperformed the other tested ensemble models, as well as a series of generalized models produced using all available input variables and member models. The model also performed well against other ensemble techniques and against the individual member models. These results demonstrate the viability of the proposed methodology in constructing a hybrid ensemble model for the forecasting of ice jams on Northern Canadian Rivers. The techniques utilised can be adapted to other locations to facilitate ice jam forecasting, requiring data that is easily available and monitored in advance of any potential flooding events. • Forecasting ice jams is challenged by both data availability and model formulation • A hybrid ensemble modelling framework was developed to address these issues • The proposed method leverages diverse machine learning algorithms • The proposed framework outperformed other tested machine learning techniques [ABSTRACT FROM AUTHOR]