Search

The ability to predict global horizontal irradiance (GHI) in the wake of changing weather conditions is becoming increasingly important in driving economic growth in the renewable energy industry. The choice of relevant meteorological variables for predicting GHI is crucial because it influences the prediction accuracy due to various geographical conditions. To fulfill this aim, this proposed study identifies a subset of the relevant input variables for the prediction of GHI by applying two methods Feature Combination (FC) and Feature Selection (FS). The results reveal that the most significant input variables for predicting GHI are Solar Zenith Angle, Dew Point, Diffuse Horizontal irradiance, Direct Normal Irradiance, and Wind speed obtained by the FS method. The predictive performance of the selected features is evaluated by feeding them into six different types of Machine Learning (ML) regressor algorithms such as Multiple Linear regressor (MLR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Light Gradient Boost Machine (LGBM) and Extra Tree (ET). The performances of models are evaluated by using statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). ET regressor gives the best prediction performance among all the six models. It is observed that the percentage reduction in error for the FS method as compared to the FC method in terms of the statistical indicators MAE, RMSE, and MAPE are 8.5%, 7.72%, and 25.84% respectively. It shows that the FS method gives better predictive performance than the FC method for the estimation of GHI. [ABSTRACT FROM AUTHOR]

• Performance assessment and production estimation of grid-connected PV station. • Strong relationship between weather variables and the output of the PV station. • Strong interdependence of the meteorological parameters and performance parameters. • The distinction between the different variable correlation results is a hard task. • Feature selection and PCA analysis were used as dimensional reduction techniques. • Dimensionality reduced data was used for training random forest models. Meteorological variables have an important effect on the performance of a grid-connected photovoltaic station, in this paper, the impact of meteorological variables on the 6 mWp grid-connected photovoltaic station in the desert of Adrar has been explored through performance assessment and output power forecasting. The impact of the meteorological variables on performance parameters has been investigated by performing interdependence and correlation studies. A clear interdependence between some variables has been observed, but the complete separation between each variable correlation effect has proven to be a difficult task. A combination of random forest method and pre-processing techniques namely feature selection and Principal component analysis has been developed in order to predict power production using meteorological variables as inputs. The forecasting models have been evaluated in terms of computation time, accuracy, and several statistical indicators. [ABSTRACT FROM AUTHOR]

• Use feature selection to extract the most discriminating features. • Improve accuracy and reduce bias using an ensemble technique. • A One-vs-Rest Consensus Learning (OvRCL) technique is proposed. Research in crash severity prediction is necessary to allow safety planners to take precautionary measures and enable first aiders to remain prepared for assisting the injured. Existing literature in the field of crash severity prediction is mostly focused on generating the attributes for predicting the severity. However, in reality, not all features are discriminating, and certain classes are challenging to detect even employing the entire feature set. Although to tackle these problems several techniques are developed in the Machine Learning (ML) literature. But their application to crash severity prediction and an optimal strategy for the best combination of features or classifiers for achieving high accuracy is a less studied area. To address these problems, this work first provides a comparison of widely used classifiers for predicting crash severity; and secondly, by combining class-wise majority voting with One-vs-Rest (OvR) approach, a novel classification framework named, OvR consensus learning (OvRCL) is proposed. The proposed method avail a feature selection technique, Mutual information (MI), to acquire the most relevant feature set regarding the output class (i.e. severity). Moreover, to differentiate each class in the multi-class data, OvRCL iteratively runs ML algorithms as binary classifiers in an ensemble framework to significantly ameliorate classification performance. In our experiments, we use four classifiers, namely, the k -Nearest Neighbors (k -NN), Support Vector Machines (SVM), Random Forest (RF), and Bagging classifier, to get the consensus. Analysis was done using a real crash dataset obtained from an open data source of Leeds city council. A four-year crash data (2015–2018) is used for training and the OvRCL is tested on the 2019 data. Moreover, to validate the performance of OvRCL, this study also utilizes two more datasets with high-class imbalance. In contrast to conventional ML algorithms, our experiments depict that the OvRCL is a potent method for forecasting crash severity levels on the data under test. [ABSTRACT FROM AUTHOR]

• Forecast electricity demand considering residents' psychological preferences. • Stationarity time series are established to avoid the impact of irregular factors. • Personalizes exogenous variables, combines correlations and importance. • A hybrid machine learning based model is built to improve forecasting performance. • Multiple implications are provided for the government and institutions. Faced with the explosive growth of population and rapid development of industrialization, household electricity demand (HED) has recently become an essential sector of energy demand. Very-short-term forecasting of HED is closely related to monitoring insecurity, detecting emergency and potential crisis. Consider challenges triggered by the characteristics of time series, firstly, a phase of data pre-processing with stationarity of time series is constructed to manage irregular trend factors. Subsequently, in the feature selection phase, a two-layer feature selection method is proposed with the combination of random forest and autocorrelation analysis to select the most relevant features, which simultaneously considers correlations and importance of input lagged variables as well as exogenous variables. After that, hybrid HED forecasting methods are constructed with incorporating multiple machine learning techniques in the forecasting phase, namely k-nearest neighbor, support vector regression , random forest and multilayer perceptron, using their respective advantages of managing different situations. As residents' psychological preferences and calendar variables highly affect future HED, this paper intends to add and test their effects on HED. Finally, the proposed methods are examined on a 10-min-ahead horizon application, whose forecasting results, comparison analyses and an extended experiment are given to illustrate their effectiveness, after which some implications are provided to show the insights gained from the results. [ABSTRACT FROM AUTHOR]

[Display omitted] • Predictive model for average dissolved oxygen (DO) levels is robust and reliable. • Maintaining optimal flocculation level in pond helps in healthy growth of shrimp. • Feature selection via Mutual Information is highly accurate for DO prediction. • Including Meteorological parameters in model development improves model accuracy. The present study approaches towards the feasibility of prediction and forecasting of dissolved oxygen (DO) and biofloc amount using the state of art machine learning algorithms in a shrimp culture system. The study was carried out considering; average DO and biofloc amount as target parameters in a shrimp culture system. There were seventeen numbers of culture and meteorological parameters considered and three different feature selection techniques used to create twelve different data subsets for model development. The model development was carried out using three popular machine learning algorithms viz., Random Forest, Adaboost and Deep neural network. The totals of thirty-six different models were obtained and their accuracies were evaluated with seven model validation tests and results were obtained and discussed. Out of thirty-six models, Random Forest technique applied model for prediction of dissolved oxygen with combined culture and meteorological parameters (R2value – 0.709, prediction accuracy – 98.26%, score – 0.7381) was found to be the best one for predictive model development. Moreover, exploratory data analysis was carried out for prediction and a framework for prediction of DO and biofloc amount in a shrimp based biofloc culture system was developed. The dissolved oxygen was found to be more robust in the predictive model development. The Intelligent Framework was developed based on the study conducted to understand and carryout prediction in farming system in a scientific manner. The developed framework can help the literate farmers and new entrants of shrimp farming to devise their own prediction models suitable for the farming conditions. [ABSTRACT FROM AUTHOR]

• To improve the classification accuracy and lower classification time in diagnosing coronary artery disease as compared to the state-of-the-art techniques. • Random Forest based Boruta feature selection algorithm and SVM feature importance is employed. • The heterogeneous ensemble algorithm is proposed for diagnosing and prediction of CAD using the Z-Alizadeh Sani dataset. • A highest classification accuracy of 98.97% for the original dataset and an accuracy of 100% for the balanced dataset is achieved in the diagnosis of CAD. Background and Objective: Coronary artery disease (CAD) is considered one of the most prominent health issues causing high mortality in the world population. Hence, earlier diagnosis and prediction of CAD is essential for the proper medication of patients. The objective of this study is to develop a machine learning algorithm that will help in accurate diagnosis of CAD. Methods: In this paper, we have proposed a novel heterogeneous ensemble method combining three base classifiers viz., K-Nearest Neighbour, Random Forest, and Support Vector Machine for effective diagnosis of CAD. The results of base classifiers are combined using ensemble voting technique based on average-voting (AVEn), majority-voting (MVEn), and weighted-average voting (WAVEn) for prediction of CAD. The random forest-based Boruta wrapper feature selection algorithm and feature importance of SVM are used for relevant feature selection based on attribute importance and rank. Results: The proposed ensemble algorithm is developed using 5 features selected based on the feature importance and the performance of the algorithm is evaluated using the Z-Alizadeh Sani dataset. Further, the dataset is balanced using Synthetic Minority Over-sampling Technique and its performance is evaluated. The result analysis shows that the WAVEn algorithm achieves better classification accuracy, sensitivity, specificity and precision of 98.97%, 100%, 96.3% and 98.3% respectively for the original dataset. The WAVEn algorithm applied on the balanced dataset achieves 100% accuracy, sensitivity, specificity and precision in diagnosing CAD. To the best of author's knowledge, the accuracy achieved by WAVEn is the highest accuracy when compared with the state-of-the-art algorithms in the literature for both original and balanced dataset. Conclusions: The statistical results prove the robustness of the WAVEn algorithm in reliably discriminating the CAD patients from healthy ones with high precision, and therefore it can be used for developing a decision support system for diagnosing CAD at an early stage. [ABSTRACT FROM AUTHOR]

• A standard flowchart from raw data collection to AI algorithm-based model development is proposed. • The interaction between shield machine and ground is simulated using AI algorithm. • The application scopes of machine learning and deep learning algorithms is investigated. The interaction between a shield machine and the ground is a complicated problem involving numerous extrinsic and intrinsic factors. Machine learning (ML) algorithms have been recently employed to predict tunnel-soil interactions. This study introduces a more powerful algorithm termed the deep learning (DL) long short-term memory (LSTM) neural network, to identify the interaction between a shield machine and the ground; this network can predict tunnelling-induced maximum settlement, the longitudinal settlement curve, and shield operational parameters. In addition, the generalisation ability of LSTM is comprehensively compared with that of a conventional ML algorithm—random forest (RF)—based on field records collected from two practical tunnel projects. A standard process of developing an ML or DL algorithm-based model, including the pre-processing of raw data, feature selection, determination of optimum hyper-parameters, and evaluation of prediction performance and generalization ability, was introduced. The results indicated that the RF-based model performs capably in terms of predicting tunnelling-induced maximum settlement, and that the LSTM-based model is suitable for predicting longitudinal settlement curves and shield operational parameters. Furthermore, the generalization ability of the LSTM-based model is better than that of the RF-based model. The strong robustness of the LSTM-based model enables its application in different types of tunnel projects. [ABSTRACT FROM AUTHOR]

The present study aims to compare the performance of two machine learning techniques that can unveil the relationship between the input and target variables and predict the ground vibration (peak particle velocity, PPV) due to quarry blasting. To this end, a Random Forest (RF) model and a Bayesian Network (BN) model were developed. Before developing these models, and in order to illustrate the necessity of proposing new intelligent systems, a new empirical equation is proposed, using maximum charge per delay and distance from the blast-face. The results confirm that there is indeed a need to develop intelligent systems with more input parameters. Thus, a Feature Selection (FS) model is applied to decrease the dimensionality of data and remove the irrelevant data. The outputs of this technique set five parameters, hole depth, power factor, stemming, maximum charge per delay and distance from the blast-face, as the most important model inputs necessary to predict PPV. After constructing FS-BN and FS-RF models and comparing them under different conditions (i.e., computational cost, accuracy and robustness), it is found that the developed FS-RF model can be introduced as a new model in the field of blasting environmental issues. The accuracy level of the FS-RF model is quite high; 92.95% and 90.32% for the train and test stages, respectively, while 92.95% and 87.09% accuracy is calculated for train and test of the FS-BN model. Thus, both developed hybrid models can effectively unveil the relationships between the input and target variables. • We measured the most effective parameters on peak particle velocity induced by blasting. • A new peak particle velocity empirical equation was proposed and compared with previous equations. • A feature selection model was applied to remove the irrelevant predictors. • Two hybrid models were developed to predict peak particle velocity. • A comparison between these models was made and the best among them was chosen. [ABSTRACT FROM AUTHOR]