This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine lear...This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.展开更多
Heart disease continues to be a major global cause of death,making the devel-opment of reliable prediction models necessary to enable early detection and treatment.Using machine learning to improve prediction accuracy...Heart disease continues to be a major global cause of death,making the devel-opment of reliable prediction models necessary to enable early detection and treatment.Using machine learning to improve prediction accuracy,this study investigates the use of the Extra Tree(Extremely Randomized Trees)algorithm for heart disease prediction.The research includes data preparation,model training,and performance evaluation using measures like accuracy,precision,recall,and F1-score.It makes use of a dataset that includes a variety of medical and demographic variables.The Extra Tree model outperforms a number of baseline models in terms of accuracy and predictive power.The dataset was obtained from the University of California,Irvine(UCI)Machine Learning Repository,which contains about 319,796 instances and 18 attributes related to heart disease.The attributes serve as the features.This study reduced the number of features from 18 to 7,by using recursive feature elimination method,which uses Random Forest as an estimator.The Extra Tree model demonstrates great performance,showing high accuracy,precision,recall,and f1 scores of 93.1%,94.8%,100%and 93.1%respectively on a dataset split ratio of 80%to 20%train set and test set respectively.The study concluded that the model may be implemented into a clinical decision support system to help healthcare providers diagnose cardiac disease.Furthermore,the feature importance analysis can help direct future research into finding the most significant risk factors for cardiovascular disease.展开更多
This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement wh...This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.展开更多
Scientists have introduced new methods for capturing energy from ocean waves.Specifically,scientists have focused on a type of wave energy converter(WEC)that is nonbuoyant(i.e.,a body that cannot float).Typically,the ...Scientists have introduced new methods for capturing energy from ocean waves.Specifically,scientists have focused on a type of wave energy converter(WEC)that is nonbuoyant(i.e.,a body that cannot float).Typically,the WEC is most effective when it is in resonance,which occurs when the natural frequency of the WEC aligns with that of the ocean waves.Therefore,accurately predicting the movement of the WEC is crucial for adjusting its system to resonate with the incoming waves for optimal performance.In this study,artificial intelligence techniques,such as random forest,extra trees(ET),and support vector machines,are created to forecast the vertical movement of a nonbuoyant WEC.The developed models require two variables as input,namely,the water wave height and its time period.A total of approximately 4500 data points,which include nonlinear water wave height and duration ob-tained from a laboratory experiment,are used as the input for these models,with the resulting vertical movement as the output.When comparing the three models based on their processing speed and accuracy,the ET model stands out as the most efficient.Ultimately,the ET model is tested using data from a real ocean setting.展开更多
The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations...The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations traversing shallow waters,where ice-berg keels may reach the seabed,potentially damaging subsea structures.Consequently,costly and time-intensive iceberg manage-ment operations,such as towing and rerouting,are undertaken to safeguard subsea and offshore infrastructure.This study,therefore,explores the application of extra tree regression(ETR)as a robust solution for estimating iceberg draft,particularly in the preliminary phases of decision-making for iceberg management projects.Nine ETR models were developed using parameters influencing iceberg draft.Subsequent analyses identified the most effective models and significant input variables.Uncertainty analysis revealed that the superior ETR model tended to overestimate iceberg drafts;however,it achieved the highest precision,correlation,and simplicity in estimation.Comparison with decision tree regression,random forest regression,and empirical methods confirmed the superior perfor-mance of ETR in predicting iceberg drafts.展开更多
基金funded by Vietnam National Foundation for Science and Tech-nology Development(NAFOSTED)under Grant No.105.99-2019.309.
文摘This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.
文摘Heart disease continues to be a major global cause of death,making the devel-opment of reliable prediction models necessary to enable early detection and treatment.Using machine learning to improve prediction accuracy,this study investigates the use of the Extra Tree(Extremely Randomized Trees)algorithm for heart disease prediction.The research includes data preparation,model training,and performance evaluation using measures like accuracy,precision,recall,and F1-score.It makes use of a dataset that includes a variety of medical and demographic variables.The Extra Tree model outperforms a number of baseline models in terms of accuracy and predictive power.The dataset was obtained from the University of California,Irvine(UCI)Machine Learning Repository,which contains about 319,796 instances and 18 attributes related to heart disease.The attributes serve as the features.This study reduced the number of features from 18 to 7,by using recursive feature elimination method,which uses Random Forest as an estimator.The Extra Tree model demonstrates great performance,showing high accuracy,precision,recall,and f1 scores of 93.1%,94.8%,100%and 93.1%respectively on a dataset split ratio of 80%to 20%train set and test set respectively.The study concluded that the model may be implemented into a clinical decision support system to help healthcare providers diagnose cardiac disease.Furthermore,the feature importance analysis can help direct future research into finding the most significant risk factors for cardiovascular disease.
基金The authors would like to thank Covenant University Centre for Research Innovation and Discovery(CUCRID)Ota,Nigeria for its support in making the publication of this research possible.
文摘This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.
文摘Scientists have introduced new methods for capturing energy from ocean waves.Specifically,scientists have focused on a type of wave energy converter(WEC)that is nonbuoyant(i.e.,a body that cannot float).Typically,the WEC is most effective when it is in resonance,which occurs when the natural frequency of the WEC aligns with that of the ocean waves.Therefore,accurately predicting the movement of the WEC is crucial for adjusting its system to resonate with the incoming waves for optimal performance.In this study,artificial intelligence techniques,such as random forest,extra trees(ET),and support vector machines,are created to forecast the vertical movement of a nonbuoyant WEC.The developed models require two variables as input,namely,the water wave height and its time period.A total of approximately 4500 data points,which include nonlinear water wave height and duration ob-tained from a laboratory experiment,are used as the input for these models,with the resulting vertical movement as the output.When comparing the three models based on their processing speed and accuracy,the ET model stands out as the most efficient.Ultimately,the ET model is tested using data from a real ocean setting.
文摘The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations traversing shallow waters,where ice-berg keels may reach the seabed,potentially damaging subsea structures.Consequently,costly and time-intensive iceberg manage-ment operations,such as towing and rerouting,are undertaken to safeguard subsea and offshore infrastructure.This study,therefore,explores the application of extra tree regression(ETR)as a robust solution for estimating iceberg draft,particularly in the preliminary phases of decision-making for iceberg management projects.Nine ETR models were developed using parameters influencing iceberg draft.Subsequent analyses identified the most effective models and significant input variables.Uncertainty analysis revealed that the superior ETR model tended to overestimate iceberg drafts;however,it achieved the highest precision,correlation,and simplicity in estimation.Comparison with decision tree regression,random forest regression,and empirical methods confirmed the superior perfor-mance of ETR in predicting iceberg drafts.
