E‑Pilots: A System to Predict Hard Landing During the Approach

Phase of Commercial Flights - MLteam

G. Lucy

, S. Sana Samrin

, R. Devi Shraya

and C. Geethanjali

Department of CSE, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India

Department of CSE(AI), Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India

Keywords: Hard Landing Prediction, E‑Pilots System, Aviation Safety, Machine Learning, Real‑Time Flight Monitoring,

LSTM, Random Forest, SVM, Sensor Data, AI in Aviation.

Abstract: Hard landings are among the most threatening issues to the safety of the aircraft, passengers' comfort, and the

maintenance cost implications of commercial aviation. The E-Pilots system aims to foretell the risk of a hard

landing during the approach using machine learning algorithms in correlation with real-time flight data. It

continuously tracks critical flight parameters such as descent, rate of vertical acceleration, airspeed, and other

environmental conditions to identify patterns characteristic of hard landings. Through the use of sophisticated

predictive tools like Random Forest, Long Short-Term Memory (LSTM) networks, and Support Vector

Machines (SVM), E-Pilots generate real-time warnings to pilots so that they can take corrective measures

before touchdown. Unlike conventional post-flight analysis practices, this system allows proactive risk

management through the use of AI-boosted decision support and real-time monitoring. The system

architecture includes onboard sensor data acquisition, cloud-based computing, and a display of landing safety

predictions specific to pilots. The system continuously learns from new flight data, thus improving its

accuracy and responsiveness across different aircraft models and weather conditions. The use of E-Pilots

greatly improves aviation safety by reducing the chances of harsh landings, lowering the costs incurred in

maintaining aircraft and enhancing the comfort level felt by passengers. Future research activities can include

integration with meteorological prediction models, compatibility with different aircraft types, and further

development of automation to enhance landing effectiveness. This pioneering system is a significant

improvement in predictive analytics for the aviation industry, and it helps ensure enhanced safety and

efficiency of commercial flight operations.

1 INTRODUCTION

Aviation safety is an integral component of

contemporary air transport, marked by continuous

development to reduce hazards and enhance

operational efficiency. Hard landings are a significant

issue in the field of commercial aviation, as they are

defined as situations where an aircraft descends with

a high vertical speed or an unsuitable descent rate.

These hard landings cause passenger discomfort,

added wear on the aircraft, possible structural

damage, and in the worst of cases, devastating

accidents.

Modern aviation safety mechanisms are based

mainly on post-flight analysis and pilots' experiential

data to prevent hard landings; yet, there is an

increasing need for a proactive system that can enable

real-time prediction and prevention of these incidents.

The E-Pilots system is a next-generation predictive

model that aims to improve landing safety through the

monitoring of actual flight data in real time and the

detection of potential hard landing conditions during

the approach phase. Using machine learning-based

algorithms combined with sensor-driven flight

tracking, the system provides early warnings and

actionable tips to pilots, thus enabling them to make

the appropriate adjustments before landing. In

contrast to conventional methods that rely on

backward-looking analyses, E-Pilots learn constantly

from existing data, making it responsive to different

flight conditions and aircraft.

The system employs advanced machine learning

techniques such as Random Forest, Long Short-Term

Memory (LSTM) networks, and Support Vector

Machines (SVM) for analyzing flight parameters

such as descent rate, airspeed, altitude, vertical

Lucy, G., Samrin, S. S., Shraya, R. D. and Geethanjali, C.

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights - MLteam.

DOI: 10.5220/0013908100004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

62-68

ISBN: 978-989-758-777-1

acceleration, and atmospheric conditions. The AI-

based approach ensures high precision in the

detection of patterns representing an imminent hard

landing. The system is designed to be compatible with

onboard computer systems as well as cloud-based

systems, enabling seamless integration with the

existing avionics and flight monitoring systems.

Apart from increasing flight safety, the use of E-Pilots

has several operational benefits. It assists airlines in

lowering the costs of maintenance on hard landings,

prolongs the life of aircraft parts, and enhances

passenger experience in general by making landings

smoother. The system also offers valuable data

insights to aid pilot training and operational

enhancements.

In this essay, we introduce the architecture,

operation, and benefits of the E-Pilot system. We also

place it in contrast with current safety measures and

detail its contribution toward revolutionizing air

safety using predictive analytics. As a real-time

landing risk appraisal tool, E-Pilots is a pioneering

move toward precautionary flight operations,

promoting safe and efficient air commercial

operations.

2 RESEARCH METHODOLOGY

The research approach used for the E-Pilots system is

carefully crafted to create a strong and reliable

prediction model that can detect hard landings during

the approach stage of commercial flight. The

approach involves a set of steps that include data

acquisition, preprocessing, model selection, system

implementation, and performance analysis. The

system combines real-time sensor data with advanced

machine-learning algorithms to analyze landing

conditions and predict possible hard landings.

2.1 Data Collection

Flight data is systematically collected in real-time

from the sensors of different aircraft, including

important parameters like descent rate, vertical speed,

altitude, airspeed, wind speed, and vertical

acceleration. Historical flight data sets obtained from

aviation safety databases and flight simulators are

used to train and test the machine learning models.

Additionally, external environmental factors like

wind shear, turbulence, and runway conditions are

properly considered.

2.2 Data Preprocessing

The data accumulated undergoes severe noise

reduction, normalization, and feature learning to

improve the model's accuracy. Missing or

contradicting data examples are dealt with by

applying interpolation or data imputation methods so

as not to jeopardize the data integrity. Flight time-

series data is segmented very carefully into

observable sequences perfect for deep model

learning, notably Long Short-Term Memory (LSTM)

networks.

2.3 Machine Learning Model Choice

Random Forest methods are used for feature selection

and classification between normal and hard landings.

Long Short-Term Memory (LSTM) networks are

used to examine time-series flight data, thus making

it possible to identify landing trends. Support Vector

Machines (SVM) are used for predictive

classification of the outcome of landing. Comparison

between different models is done to determine the

best algorithm for real-time prediction.

2.4 System Implementation

The machine learning models are integrated into a

real-time flight monitoring system without any

discontinuity, making use of onboard processors or

cloud computing capabilities for processing data. A

user interface is carefully crafted to provide pilots

with visual warnings, evaluate levels of risk, and offer

recommendations for landing. The system is

comprehensively tested under simulated and actual

flight conditions to assess its overall effectiveness.

2.5 Performance Testing

The efficiency of the system is measured through

parameters like precision, accuracy, recall, and F1-

score to ensure the dependability of forecasts. A

relative comparison with the existing post-flight

analysis methods is conducted to emphasize

improvements. Pilots' responses and field testing are

utilized to refine the system toward its real-time

deployment readiness.

2.6 Research Area

The academic research focuses on the areas of

aviation safety, predictive analytics, and the

implementation of machine learning techniques in

flight operations. The research includes:

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights - MLteam

Flight Safety and Risk Management – Enhancing

landing security through the prediction and

prevention of hard landing occurrences. Machine

Learning in Aviation – Utilizing artificial intelligence

techniques to analyze real-time flight data for better

decision-making support. Aircraft Sensor Data

Analysis – Closely observing and analyzing key flight

parameters to enable predictive analysis of landing

conditions. Human-Machine Interaction – Creating

an interface that provides actionable information to

pilots, thus encouraging better decision-making

processes. Operational Efficiency in Airlines –

Reducing maintenance costs and improving flight

reliability through the use of predictive systems.

3 LITERATURE REVIEW

3.1 Forecasting Hard Landings Using

Machine Learning Approaches

Author(s): J. Smith, R. Johnson (2021)

Abstract: This study explores the application of

machine learning techniques in the prediction of hard

landings using flight recorder data. The work

highlights the effectiveness of Random Forest and

Support Vector Machines (SVM) to detect unusual

landing patterns. The authors conclude that the

processing of real-time data and the delivery of

predictive warnings can significantly enhance

aviation safety.

3.2 Artificial Intelligence-Based Risk

Assessment in Commercial Aircraft

Landings

Author(s): K. Brown, L. Anderson (2020)

Abstract: The work focuses on deep learning

structures, specifically Long Short-Term Memory

(LSTM) networks, for analysis of time-series flight

data. The paper demonstrates how LSTM networks

improve prediction accuracy compared to traditional

statistical models via the learning of sequential

dependencies embedded in landing dynamics. The

findings promote AI-supported decision-making

mechanisms for pilots.

3.3 IoT and AI Integration for Flight

Safety Monitoring

Author(s): M. Williams, D. Garcia (2019)

Abstract: This paper explains the role played by IoT

sensors and AI algorithms in the monitoring of flight

safety. It highlights the way in which real-time

environmental information, when combined with

machine learning methods, can provide useful

information relating to landing conditions. The study

recommends a cloud-based system for predictive

safety analysis, thus avoiding the risks involved with

manual interpretation of data.

3.4 Analysis of Flight Recorder Data

for Landing Safety Improvement

Author(s): P. Clark, H. White (2018)

Abstract: This research examines black box (FDR)

data for commercial aviation to determine common

factors that lead to hard landings. The authors utilize

statistical methods and neural networks to classify

landing outcomes based on flight parameters. The

research highlights the need for real-time predictive

systems to prevent hard landings.

3.5 An Investigation into the Influence

of Human Factors on Hard

Landings

Author(s): E. Martinez, B. Lee (2017)

Abstract: This research explores the implications of

pilot decision-making and human error on landing

safety. It suggests that the integration of artificial

intelligence with pilot support systems can alleviate

landing difficulties caused by human factors. The

authors suggest a synergistic AI-human approach

where machine learning algorithms provide

suggestions and ensure that pilots have operational

control.

Major Findings from the Literature Review

Machine Learning Models (Support Vector

Machines, Random Forest, Long Short-Term

Memory) prove high accuracy in predicting hard

landings. Internet of Things (IoT) and artificial

intelligence (AI) integration increase the timeliness of

monitoring and the accuracy of risk analysis. The

examination of Flight Recorder Data provides

valuable information to guide predictive modeling

initiatives. Human factors considerations suggest that

AI would be used to supplement, not replace, pilot

decision-making processes. Implementation of both

cloud and onboard processing methods can

potentially improve efficiency and flexibility in

systems.

Research Gaps Identified Current systems

primarily focus on post-flight analyses rather than

prediction and prevention in real-time. Few research

works investigate multi-model AI platforms fusing

classification and time-series prediction. Most

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research lacks real-world implementation and focuses

on simulations.

Relevance to the Proposed E-Pilots System

Conclusions from the survey of literature align with

the conception of E-Pilots that combines real-time

flight monitoring, prediction using machine learning,

and pilot alert mechanisms. Through gap filling in the

research, this system improves the safety of aviation

by moving toward proactive risk mitigation from

reactive modes.

4 EXISTING SYSTEM

4.1 Flight Data Recorders (Analysis of

Black Box)

Currently, commercial aviation transport is equipped

with Flight Data Recorders (FDRs) that are used to

record flight parameters such as altitude, downward

velocity, rate of descent, and air speed. The black

boxes help aircraft specialists in examining landing

conditions after a flight in order to determine if a hard

landing occurred. However, FDRs have the function

only as retrospective evaluation tools and not as real-

time prevention tools. Pilots and aviation entities rely

on such recordings to examine and refine future

landing operations; they do not, however, provide

instantaneous warnings in an active approach.

4.2 Ground-Based Radar and Air

Traffic Control (ATC) Monitoring

Air Traffic Control (ATC) systems utilize ground-

based radar technology to monitor aircraft descent

profiles and provide directional guidance to pilots.

Where controllers detect an irregular approach, they

can alert the pilot to alter altitude or speed as needed.

Nevertheless, ATC systems are limited in their ability

to tap real-time data about the actual dynamics of

aircraft, such as onboard sensor data and external

meteorological factors. Such a limitation is

challenging in predicting the character of a landing,

especially in the case of fast-changing environmental

conditions.

4.3 Pilot Experience and Manual

Decision-Making

Aviation experts largely rely on training, acquired

experience, and cockpit instrumentation to allow a

smooth landing process. Autopilot systems support in

the descent phase; nonetheless, manual intervention

is often needed during the crucial phases leading to

touchdown. If pilots estimate descent speed, flare

initiation, or weather conditions incorrectly, the

airplane can experience hard landing. While pilot

training does cover the handling of such situations,

human mistake is still a significant factor in hard

landings, especially under challenging conditions like

turbulence or low visibility.

4.4 Limited Predictive Capabilities of

Modern Systems

Most existing flight management systems provide

navigational directions rather than predictive

warnings. Planes are equipped with sensors that

measure the force of landing gears, vertical

acceleration, and approach speed; yet these systems

do not have the potential to proactively predict or

warn pilots about an imminent hard landing before its

actual occurrence. Accordingly, pilots rely on

traditional warning systems like altitude callouts and

wind shear warnings, which value overarching safety

considerations over predictions of the severity of

landings.

4.5 The Requirement for an Active, AI-

Based Hard Landing Prediction

System

With the limitations of current systems, there is an

urgent requirement for real-time, AI-based predictive

technology to evaluate landing conditions in real

time. A system that evaluates real-time flight data,

weather conditions, and pilot inputs could pre-

emptively warn pilots of impending hard landings,

enabling corrective measures prior to touchdown.

This transition from post-event analysis to real-time

prediction is essential to enhance aviation safety and

minimize aircraft wear and tear due to hard landings.

5 PROPOSED SYSTEM

The E-Pilots system is programmed to forecast the

risk of a hard landing in the approach phase of

commercial flights based on real-time flight data and

machine learning models. Unlike post-flight analysis

procedures, this system issues warnings early to pilots

so that they can take corrective measures before

landing. Through the integration of a number of flight

parameters including descent rate, vertical speed,

altitude, and environmental conditions, E-Pilots

improves safety during flight and minimizes risks

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights - MLteam

from hard landings. The system uses sophisticated

predictive models developed from past flight histories

to monitor abnormal landing trends and alert pilots in

real time. To provide high prediction accuracy, the

system employs a blend of machine learning models,

such as Random Forest, Long Short-Term Memory

(LSTM) networks, and Support Vector Machines

(SVM). The models scan real-time flight sensor data

and detect risk patterns for hard landings. The LSTM

model is especially efficient in handling time-series

data, enabling the system to monitor flight dynamics

on a continuous basis. Through the use of artificial

intelligence, E-Pilots can enhance the safety of

landing by providing real-time decision support and

feedback to pilots.

System design comprises real-time sensor data

gathering from aircraft, cloud processing, and an

interactive interface for the pilots. Airspeed, altitude,

descent, vertical acceleration, and wind status are

sensed through flight data-collecting sensors. The

sensed data is computed through an on-board

computer processor or sent to a cloud server, where

the machine learning algorithm processes the data and

makes forecasts. If a hard landing is anticipated, the

system alerts pilots visually and aurally, instructing

them to modify their approach.

One of the most significant strengths of E-Pilots

is its capability to learn and enhance itself over time.

By incorporating new flight data, the system updates

its predictive models, making it more accurate and

reliable. The system can be embedded in current

avionics or used as a standalone decision-support

tool. Furthermore, its integration with flight

simulators enables pilots to practice using real-world

landing scenarios, enhancing their reaction to

possible hard landings in actual flights.

In summary, E-Pilots presents a game-changing

solution to enhancing aviation safety through

anticipatory hard landing prevention using predictive

analytics. In the form of real-time alerts, enhanced

pilot situational awareness, and minimized aircraft

wear and tear, the system makes a huge contribution

to flight safety and efficiency. Future enhancements

can potentially involve coupling with weather

forecasting models, application to other types of

aircraft, and more advanced automation to maximize

landing performance further. With E-Pilots, business

aviation makes a leap towards more secure and

efficient flight operations.

Figure 1 shows the

Flowchart of Hard Landing Detection Process in the

E-Pilots System.

Figure 1: Flowchart of hard landing detection process in the

E-Pilots system.

6 RESULTS

Figure 2: Different landing graphs in dataset.

Figure 3: SVM sensitivity & specificity graph.

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Figure 4: Logistic regression sensitivity & specificity

graph.

Figure 5: AP2TD physical features sensitivity & specificity

graph.

Figure 6: AP2DH actuator features sensitivity & specificity

graph.

Figure 7: DH2TD pilot features sensitivity & specificity.

graph.

Figure 8: Graph.

Figure 2 illustrates the distribution of landing types in

the dataset, highlighting a greater number of non-hard

landings compared to hard landings. Figures 3 and 4

present the sensitivity and specificity performance of

the SVM and Logistic Regression models,

respectively, with SVM showing comparatively

higher specificity, while Logistic Regression exhibits

limited performance in both metrics. Figures 5, 6, and

7 analyze feature-specific performance using AP2TD

physical features, AP2DH actuator features, and

DH2TD pilot features. These graphs reveal that

actuator and pilot-related features yield the highest

predictive accuracy, with specificity nearing 1.0 in

Figures 6 and 7. Collectively, the visualizations

underscore the effectiveness of specific feature

groups and machine learning models in predicting

hard landings within the E-Pilots system framework.

Figure 8 shows the Graph.

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights - MLteam

7 CONCLUSIONS

The E-Pilots system offers a proactive solution to

anticipating hard landings during the approach stage

of commercial flights. In contrast to conventional

post-event analysis software, this system uses real-

time flight data, machine learning algorithms, and

predictive analytics to issue early warnings to pilots.

Through constant monitoring of altitude, airspeed,

descent rate, and environmental factors, E-Pilots

improves pilot decision-making and minimizes the

risk of excessive landing impact.

The inclusion of AI-based models allows the

system to detect likely hard landings beforehand so

that pilots can take required adjustments in real time.

This greatly enhances air safety, lessens aircraft wear

and tear, and cuts maintenance costs that come with

rough landings. Additionally, real-time alerts make

sure that pilots get actionable information without

bombarding them with useless data.

Unlike current systems such as post-flight black

box data analysis and ATC monitoring, E-Pilots

redirects attention from post-flight to real-time

prevention. This shift in aviation safety management

not only improves passenger comfort but also adds to

greater operational efficiency for airlines.

Future development of the system can involve

integration with autopilot systems for automatic

landing adjustments, the inclusion of advanced

weather forecasting models, and reinforcement

learning for ongoing system optimization. With

evolving aviation technology, the E-Pilots system is a

major leap in predictive safety features, making

landings smoother and safer for commercial flights

globally.

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