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... the average departure delay of the same days are among the highest as seen in Figure 3. Thus the chances of getting delayed on a flight are higher on those days. Departure Delay times also varies by flight carriers, it was observed that carriers WN, F9 and UA have more delayed flights than other carriers as shown in Figure 4. Airport activity plays a crucial part in determining if a flight will be delayed, as busier airports generally handles more traffic, but counterintuitively such airports will have better logistics to handle such large Figure 5 shows the departure delay from the five most busiest airport. In Denver (Airport ID 11292) the largest airport in the United States of America many flights are delayed by 2 minutes, although the facility has a well organized logistics. ...
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Citations
... Finally, using out-of-core computation enables data scientists to process massive amounts of information. As one of the widely preferred algorithms, XGBoost was included for its high performance and efficiency, especially in handling sparse datasets and complex patterns, as reported by [29,31,49]. ...
Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naïve Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.
... Thus, the prediction model is an ensemble of weaker prediction models. In regression problems, boosting builds a series of trees in a stepwise fashion, and then selects the optimal tree using an arbitrary differentiable loss function [26]. Like random forest, it combines many smaller, weaker models into a final summed prediction. ...
Background: Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low-and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information.
... Thus, the prediction model is an ensemble of weaker prediction models. In regression problems, boosting builds a series of trees in a stepwise fashion, and then selects the optimal tree using an arbitrary differentiable loss function [26]. Like random forest, it combines many smaller, weaker models into a final summed prediction. ...
Background
Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information.
Objective
To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.
Methods
Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.
Results
Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model’s predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.
Conclusions
This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.
... Therefore, the advantages of fuel consumption retrofits can be quantified before and after using a measure by analyzing aircraft performance parameters monitoring. Statistical analysis of historical datasets and physical estimate models was mainly used for analyzing flight parameters such as fuel consumption [11]. The Gauges of aircraft performance determined by specific operating conditions are compared to book values from the operating manual of the flight team [12]. ...
... Gaussian radial function (RBF) is used for all flight phases. It is the most widely used kernel with a width ofr as given in Eq. (11). ...
The most efficient technique that is used for aircraft engine tuning is through mounting the engine on the engine test bench (ETB) to analyze, tune and monitor its variables through the ETB run. It is practically very difficult to unmount the engine from the aircraft and mount it on the ETB for analyzing and estimating a single variable such as fuel consumption or oil temperature as the unmounting process requires huge manpower and machinery. This problem can be resolved if the fuel consumption of an air vehicle is estimated without unmounting the engine from the aircraft through applying data analytics and machine learning models. Therefore, in this paper, the fuel consumption of an aircraft is analyzed and estimated through advanced data science techniques. The dataset went through data analyzing and preprocessing techniques before applying multiple machine learning models such as multiple linear regression (MLR), support vector regression, decision tree regression and deep learning algorithm RNN/LSTM. The performance of algorithms has been evaluated using model evaluation methods such as mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination. The models are evaluated in taxi, cruise and approach flight phases where the LSTM performs excellent among all other algorithms with RMSE 15.1%, 10.5% and 0.9%, respectively.
... In recent years, machine learning (ML) algorithms have been widely used in engineering problems, especially to estimate the structural responses (Dias & Silvestre, 2011;Kaveh, 2014;Kaveh & Bondarabady, 2004;Kaveh et al., 2008;Naser et al., 2021;Patel & Mehta, 2018;Tran et al., 2019Tran et al., , 2022. Many studies have applied various predicted ML techniques such as gradient boost regression tree (GBRT) (Friedman, 2001;Hao et al., 2022;Manna et al., 2017;Qi et al., 2018), artificial neural networks (ANNs) (Manna et al., 2017;Rönnholm et al., 2005;Tran et al., 2019Tran et al., , 2022, and other hybrid optimization models (Alizamir & Sobhanardakani, 2018;Kumar et al., 2022;Shariati et al., 2019;Soltani et al., 2022;Toghyani et al., 2016). In addition, comparisons of the prediction performance of ML models were presented (Alzoubi et al., 2018;Le et al., 2019;Liu et al., 2021;Tran & Kim, 2020). ...
... In recent years, machine learning (ML) algorithms have been widely used in engineering problems, especially to estimate the structural responses (Dias & Silvestre, 2011;Kaveh, 2014;Kaveh & Bondarabady, 2004;Kaveh et al., 2008;Naser et al., 2021;Patel & Mehta, 2018;Tran et al., 2019Tran et al., , 2022. Many studies have applied various predicted ML techniques such as gradient boost regression tree (GBRT) (Friedman, 2001;Hao et al., 2022;Manna et al., 2017;Qi et al., 2018), artificial neural networks (ANNs) (Manna et al., 2017;Rönnholm et al., 2005;Tran et al., 2019Tran et al., , 2022, and other hybrid optimization models (Alizamir & Sobhanardakani, 2018;Kumar et al., 2022;Shariati et al., 2019;Soltani et al., 2022;Toghyani et al., 2016). In addition, comparisons of the prediction performance of ML models were presented (Alzoubi et al., 2018;Le et al., 2019;Liu et al., 2021;Tran & Kim, 2020). ...
... Rights reserved. (Friedman, 2001;Manna et al., 2017;. Moreover, GBRT combines the strengths of decision trees and boosting to create accurate and robust predictive models. ...
This study presents the performance of three machine learning (ML) models including gradient boosting regression trees (GBRT), artificial neural network model (ANN), and artificial neural network–particle swarm optimization (ANN-PSO) for predicting the axial compression capacity (ACC) of cold‑formed steel elliptical hollow section (EHS) columns. To achieve the goal, a set of 291 data is collected from previous studies to develop GBRT, ANN, and ANN-PSO models. The performance of GBRT, ANN, and ANN-PSO models is evaluated based on the statistical indicators, which are \({R}^{2}, \mathrm{RMSE},\mathrm{ MAPE},\) and \(i20-\mathrm{index}\). The results show that the ANN-PSO model with \({R}^{2}=1.00, \mathrm{RMSE} = 41.3631, \mathrm{MAPE }= 1.3689,\) and \(i20-\mathrm{index} = 0.9966\) has the best performance compared to GBRT and ANN models. Moreover, a graphical user interface tool is developed based on the ANN-PSO model for practical designs.
... So far, machine-learning (ML) has been applied in many fields, such as engineering, economics, and social problems (Kaveh, 2014;Kaveh & Khavaninzadeh, 2023;Kaveh & Servati, 2001;Manna et al., 2017;Naser et al., 2021;Nguyen et al., 2021a;Nguyen et al., 2021bNguyen et al., , 2022bNguyen et al., 2023;Qi et al., 2018;Tran et al., 2021;Tran & Kim, 2020;Tran et al., 2019). Meanwhile, the GBRT model emerged as a powerful regression tool. ...
... Meanwhile, the GBRT model emerged as a powerful regression tool. Particularly, it was used for prediction, analysis, and structural behaviors (Hao et al., 2022;Manna et al., 2017;Nieto et al., 2018;Prettenhofer & Louppe, 2014;Qi et al., 2018;Tran & Kim, 2022). The GBRT model was employed for predicting the behavior of concrete (Bakouregui et al., 2021;Dahiya et al., 2021;Shatnawi et al., 2022), and steel structures (Nguyen et al., 2022a;Nieto et al., 2018;Truong et al., 2020;Wang & Song, 2020). ...
... The GBRT is a machine-learning algorithm. It is a prediction algorithm based on a regression model that has been widely used in many fields (Friedman, 2001;Hao et al., 2022;Manna et al., 2017;Qi et al., 2018). The GBRT algorithm is a combination algorithm of the gradient boosting (GB) algorithm and regression tree (RT) algorithm. ...
This study presented the performance of the gradient boosted regression trees (GBRT) model for predicting the axial load capacity (ALC) of the cold-formed steel semi-oval hollow section (CFS-SOHS) columns. A total of 219 datasets were carefully collected from previous publications including experimental and numerical results, which were used to develop the GBRT model. Of these, 209 datasets were used for training, while 10 datasets were used for testing. The results obtained from the GBRT model were compared with two existing formulas based on the statistical metrics (\({R}^{2},\mathrm{ RMSE},\mathrm{ and }a20-\mathrm{index}\)). The statistical metrics from the GBRT model \({(R}^{2}=0.9999,\mathrm{ RMSE }= 29.458 ,\) and \(i20-\mathrm{index }= 1.000)\) have been the best-predicting to the ALC of the CFS-SOHS column. Finally, a new graphical user interface \(\left(\mathrm{GUI}\right)\) tool was built based on the regression model for practice design.
... To construct a concurrent computational model for aircraft delay forecast using our knowledge of big data so that additional research can be conducted to aid in the deployment of other big data systems into practical applications Classification and regression procedures are the two main methods for creating the prediction model. Recent studies have classified models using machine learning approaches, and the results have been encouraging [6]. For instance, L. Hao et al. [7] employed a regression model to estimate aircraft delay for the 3 major profitable airfields in New York. ...
... It is essential for the prediction model to have a high level of accuracy because the results of predictions with lower accuracy will mislead airport operators when it comes to understanding the future delay status of flights, which will, in turn, lead to ineffective alleviation of flight delays [8,9]. The report from the Bureau of Transportation Statistics (BTS) states that an initial flight delay can be attributed to a variety of factors, including severe weather, a breach in security, air carrier, etc. [10]. However, the high number of connected resources in the National Airspace System (NAS), such as aeroplanes, crews, and passengers, results in the propagation of flight delays [11]. ...
Numerous companies rely on a number of different airlines to connect them with other regions of the world as a result of the increasingly important position that the aviation industry plays in today's global transportation sector. On the other hand, severe weather can have a direct impact on airline services, most notably in the form of flight delays. The solution to this problem is to accurately predict these flight delays, which enables passengers to be well prepared for the deterrent that will be caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance in order to diminish the negative impact. The goal of this work, which makes use of quantum machine learning, is to investigate the methodologies that are applied in the construction of models for forecasting flight delays that are brought on by inclement weather. In the initial phase of the project, we investigate the feasibility of employing Python-based linear Regression in conjunction with Support Vector Machine. After that, we feed the dataset into our classifier to obtain the results. In the second half of the project, our primary focus is on acquiring data, and we investigate the possibility of combining quantum machine learning with the process of gathering data by first dissecting the dataset and then determining which features are important. After looking over the results, we compared them to the outcomes of other models, such as the machine learning classifier with quantum linear regression and the quantum SVM-SVR, in order to determine which classifier would be the most effective in resolving the issue.
... More recently, machine learning models have been applied to predicting delays at the flight level. Manna et al. (2017) leveraged gradient boosted decision trees, and Liu et al. ...
... This model was used to forecast both delays, i.e., arrival and departure delays. Manna et al. [25] considered a day of the week, carrier, origin-destination, airport IDs, CRS time, and delay from the BTS dataset to implement flight delay prediction. They have obtained an arrival accuracy of 92.31% and a departure accuracy of 94.85%. ...
Over the past years, flight delay has been a critical concern in the aviation sector due to the increased air traffic congestion worldwide. Moreover, it also prolongs the other flights, which can discourage users from traveling with the particular airline. As a result, we proposed a model to predict the overall flight delay using a random forest and path-finding algorithm. The proposed model focuses on searching flights (can be nonstop or connecting) between the source and destination at the earliest. The proposed model identifies the fastest flights between source and destination based on the input by the user using some open source/public APIs, which are further inserted into Neo4j to convert it into a JSON format. Finally, the experimental results on the real-time data set show the proposed model's effectiveness compared to the state-of-the-art models. The results and analysis yield an accuracy of 98.2% for delay prediction on historical data using the random forest algorithm.
