Assessment of the Impacts of Thunderstorm on Flight Operations at Murtala Mohammed International Airport,Ikeja,Lagos State,Nigeria

The inefficiency of the aviation industry and the persistent rise in aviation hazards have been linked to weather phenomena.As a result,researchers are looking for better solutions to the problem.The study examined the impact of thunderstorms on flight operations at Murtala Mohammed International Airport,Lagos.The data on thunderstorms and flight operations were sourced from Nigerian Meteorological Agency(NiMet)and Nigerian Airspace Management Agency(NAMA)respectively.In order to meet the research target,descriptive statistics(mean,standard deviation,and charts)and inferential statistics(Pearson’s Product Moment Correlation(PPMC)and Regression)were used.The significance level for all inferential analyses was set at 5%(0.05).The study revealed that 77.4%of thunderstorms occurred during the rainy season(April-October)while 22.6%occurred during the dry season(November-March).It also revealed some fluctuating movements of a thunderstorm in the study area.According to the findings,thunderstorms occur most frequently at the airport in June and less frequently in January and December.The study also discovered that thunderstorms at the airport are positively and significantly related to flight delays and cancellations,while the association between flight diversions and thunderstorm occurrence is positive but statistically insignificant.Furthermore,flight delays,flight diversions,and flight cancellations interact positively among themselves.The regression result of the study revealed that a 1%increase in thunderstorm occurrence leads to a 19.4%increase in flight delay,a 7.1%increase in flight cancellation,and a 4.3%increase in flight diversion.As a result,the study presented various regression models that may be utilized to make predictions.The study proposes consistent thunderstorm observation at the airport and steady forecasts using the regression models,based on the findings.However,it further recommends that pilots,air traffic controllers,and meteorologists be trained and retrained so that they can provide better and more efficient services.

A dual population multi-operator genetic algorithm for flight deck operations scheduling problem

It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

EMMAE failure detection system and failure evaluation over flight performance

Purpose-The purpose of this paper is to present the research into fault detection and isolation(FDI)and evaluation of the reduction of performance after failures occurred in the flight control system(FCS)during its mission operation.Design/methodology/approach–The FDI is accomplished via using the multiple models scheme which is developed based on the Extend Kalman Filter(EKF)algorithm.Towards this objective,the healthy mode of the FCS under different type of failures,including the control surfaces and structural,should be considered.It developed a bank of extended multiple models adaptive estimation(EMMAE)to detect and isolate the above mentioned failures in the FCS.In addition,the performances including the flight envelope,the voyage and endurance in cruising are proposed to reference and evaluate the process of mission,especially for UAV under failure conditions.Findings-The contribution of this paper is to provide the information not only about the failures,but also considering whether the UAV can accomplish the task for the ground station.Originality/value-The main contribution of this paper is in the areas of the structural and control surface faults researching,which are occurred in the mission procedures and emphasized the identification of those failures’magnitudes.The FDI scheme includes the performance evaluation,while the evaluation obtained through the extensive numerical simulations and saved in the offline database.As a consequence,it is more accurate and less computationally demanding while evaluating the performance.