scholarly journals Flight Delay Prediction Using Machine Learning

2021 ◽  
pp. 473-476
Author(s):  
Prajwal Dhone ◽  
Uday Kirange ◽  
Rushabh Satarkar ◽  
Prof. Shashant Jaykar
Keyword(s):  
Machine Learning ◽  
Negative Impact ◽  
Weather Conditions ◽  
Current Data ◽  
Environmental Harm ◽  
Flight Delays ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Flight Delay ◽  
Delay Prediction

In this fast growing world as airplanes continue flying, flight delays are the part of the experience. According to the Bureau Of Statistics(BOS), about 20% of all flights are delayed by 15 minutes or more. Flight delays causes a negative impact, mainly economical for airport authorities, commuters and airline industries as well. Furthermore, in the domain of sustainability, it can even cause environmental harm by the rise in fuel consumption and gas emissions and also some of the important factors including adverse weather conditions, preparing the aircraft, fixing of mechanical issue, getting security clearance, etc. Hence, these are the factors which indicates the necessity it has become to predict the delays of airline problems. To carry out the predictive analysis, which includes a range of statistical techniques from machine learning, this studies historical and current data to make predictions about the future delays, taking help of Regression Analysis using regularization technique used in Python.

scholarly journals Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 255 ◽  
Author(s):  
Antonio Mateo-Aroca ◽  
Ginés García-Mateos ◽  
Antonio Ruiz-Canales ◽  
José María Molina-García-Pardo ◽  
José Miguel Molina-Martínez
Keyword(s):  
Weather Conditions ◽  
3D Models ◽  
Base Station ◽  
Aerial Images ◽  
Image Capture ◽  
Communication Architecture ◽  
Adverse Weather Conditions ◽  
Green Cover ◽  
Adverse Weather ◽  
Flight Delay

Due to the limitations of drones and satellites to obtain aerial images of the crops in real time, the time to flight delay, the problems caused by adverse weather conditions and other issues, the use of fixed cameras placed on the regions of interest is essential to get closer, periodic and on-demand images. Water management in agriculture is one of the most important applications of these images. Top view images of a crop can be processed for determining the percentage of green cover (PGC), and 2D images from different viewing angles can be applied for obtaining 3D models of the crops. In both cases, the obtained data can be managed for calculating several parameters such as crop evapotranspiration, water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant. For this purpose, a remote image capture system has been developed for an application in lettuce crops. The system consists of several capture nodes and a local processing base station which includes image processing algorithms to obtain key features for decision-making in irrigation and harvesting strategies. Placing multiple image capture nodes allows obtaining different observation zones that are representative of the entire crop. The nodes have been designed to have autonomous power supply and wireless connection with the base station. This station carries out irrigation and harvesting decisions using the results of the processing of the images captured by the nodes and the information of other local sensors. The wireless connection is made using the ZigBee communication architecture, supported by XBee hardware. The two main benefits of this choice are its low energy consumption and the long range of the connection.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

scholarly journals Ship Handling in Unprotected Waters: A Review of New Technologies in Escort Tugs to Improve Safety

2021 ◽  
Vol 2 (1) ◽  
pp. 46-62
Author(s):  
Santiago Iglesias-Baniela ◽  
Juan Vinagre-Ríos ◽  
José M. Pérez-Canosa
Keyword(s):  
High Performance ◽  
New Technologies ◽  
Weather Conditions ◽  
Hull Form ◽  
Best Available Technologies ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
New Type ◽  
Technological Challenge ◽  
And Performance

It is a well-known fact that the 1989 Exxon Valdez disaster caused the escort towing of laden tankers in many coastal areas of the world to become compulsory. In order to implement a new type of escort towing, specially designed to be employed in very adverse weather conditions, considerable changes in the hull form of escort tugs had to be made to improve their stability and performance. Since traditional winch and ropes technologies were only effective in calm waters, tugs had to be fitted with new devices. These improvements allowed the remodeled tugs to counterbalance the strong forces generated by the maneuvers in open waters. The aim of this paper is to perform a comprehensive literature review of the new high-performance automatic dynamic winches. Furthermore, a thorough analysis of the best available technologies regarding towline, essential to properly exploit the new winches, will be carried out. Through this review, the way in which the escort towing industry has faced this technological challenge is shown.

Sign in / Sign up

Export Citation Format

Share Document