Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Predicting Beach Crowd Levels Using a Feature-Engineered Random Forest Classifier for Enhanced Accuracy

Authors
P. Priyadharshini1, *, M. K. Nivodhini1, S. Ajithkumar1, P. Pavithrasree2, P. Revathi2, N. Sahana2
1Assistant Professor, Department of Computer Science and Engineering, K.S.R. College of Engineering, Tiruchengode, Namakkal, India
2Research Scholar, Department of Computer Science and Engineering, K.S.R. College of Engineering, Tiruchengode, Namakkal, India
*Corresponding author. Email: priyadharshinip@ksrce.ac.in
Corresponding Author
P. Priyadharshini
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_143How to use a DOI?
Keywords
Beach Crowd Prediction; Random Forest Classifier; Feature Engineering; Environmental Data Analysis; Coastal Management
Abstract

Importance of Beach Crowd Prediction for proper resource management, safety, and improving beachgoers experience. In this study, we propose a hybrid approach based on feature engineering Random Forest Classifier and outperform the accuracy in predictive in crowd level. Utilizing sophisticated feature selection and engineering methods, the model integrates various contextual variable influences regarding beach attendance, environmental, social, and seasonal. Model robustness and generalization was ensured by deriving from the existing body of literature in related fields including crowd density estimation, coastal monitoring, and high-dimensional data processing. Improvements to random forest algorithms and ensemble learning methods were also incorporated into the model to maximize predictive effectiveness. The method maintains high accuracy while being efficient in terms of computing, as shown in various real-world datasets. Overall, this research presents a scalable framework for anticipating crowd levels, so beach managers and policymakers can make data-driven decisions that lead to sustainable coastal management.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_143How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Priyadharshini
AU  - M. K. Nivodhini
AU  - S. Ajithkumar
AU  - P. Pavithrasree
AU  - P. Revathi
AU  - N. Sahana
PY  - 2025
DA  - 2025/05/23
TI  - Predicting Beach Crowd Levels Using a Feature-Engineered Random Forest Classifier for Enhanced Accuracy
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1719
EP  - 1735
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_143
DO  - 10.2991/978-94-6463-718-2_143
ID  - Priyadharshini2025
ER  -