Research on the Prediction of Aircraft Landing Distance

Ningning Zhao,Junchao Zhang,Francesco Franco

doi:10.1155/2022/1436144

Abstract

To prevent aircraft from running off the runway during landing, this paper uses a BP neural network model to predict the aircraft landing distance. In this study, based on the five main influencing factors of airport height, aircraft landing quality, airport runway slope, wind, and ambient temperature, the B737-800 was selected as the reference aircraft and the relevant operational data were collected using Boeing’s LAND software for the study. In addition, this study uses LM (Levenberg–Marquardt) algorithm and GA (genetic algorithm) to optimize the training process, accelerate the computation speed, and improve the shortage of local optimization of BP (back propagation) neural network model and then construct the GA-LM-BP neural network optimization model. Finally, it makes the BP neural network have the ability of global search for optimal solutions. The results show that the predicted landing data are in good agreement with the measured landing data. The maximum absolute error is within 6.66 m and the maximum relative error is within 0.038%.

Highlights

Accidents involving aircraft running off the runway during the landing phase occur frequently and can result in injuries and economic losses
In order to ensure the safety of aircraft landing, especially for aircraft operating on wet runways and contaminated runways, the Civil Aviation Administration of China (CAAC) has formulated and issued an advisory circular on “Regulations on the Operation of Wet Runways and Contaminated Runways by Air Carriers.”
The conventional BP neural network and extreme learning machine (ELM) are compared under different evaluation indexes. e results show that the prediction of landing distance is consistent with the measured data, but the accuracy of the prediction results is not high enough [9]

Summary

Introduction

Accidents involving aircraft running off the runway during the landing phase occur frequently and can result in injuries and economic losses. Ey predicted the aircraft landing distance by considering three main factors: airport, weather, and aircraft This prediction accuracy is not high enough [5]. Qian et al proposed a landing distance prediction model based on improved extreme learning machine (ELM) with flight data. Gao et al proposed a fetal weight prediction model based on genetic algorithm optimized back propagation (GA-BP) neural network. Liu et al use the BP neural network improved by the LM algorithm to obtain the tunnel displacement back analysis model, and the calculation results show that the accuracy is as high as 97.3% [14]. Erefore, in order to achieve faster data processing speed and high accuracy prediction results, an optimized neural network model combining LM and GA algorithms is used in this study. This study integrates five factors: airport height, aircraft landing quality, airport runway slope, wind, and ambient temperature to predict aircraft landing distance with high accuracy

Optimized BP Neural Network Prediction Model

Application Analysis

Construction of the Landing Distance Prediction Model

Conclusion e following conclusions were drawn from the study of the paper: