Avian Species Population Forecaster Using Machine Learning

Likhitha Morampudi, Ramya Rajanala, Pothuri Surendra Varma

2025

Abstract

Birds help to link various ecosystems. Ecosystems like farmland, woodland, water and wetlands, wildfowl. Migration patterns link biodiversity by facilitating gene flow, spreading seeds, transferring nutrients, and maintaining ecological balance across different ecosystems. This research analyzed historical data of birds from 1960 to 2015, and forecasted the future bird's population. and focused on predicting the bird's population in various ecosystems. We employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to achieve accurate forecasting. bird population trends by integrating seasonal and temporal patterns, thereby enhancing predictive precision for ecological monitoring and conservation planning.

Paper Citation

in Harvard Style

Morampudi L., Rajanala R. and Varma P. (2025). Avian Species Population Forecaster Using Machine Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 929-935. DOI: 10.5220/0013607700004664

in Bibtex Style

@conference{incoft25,
author={Likhitha Morampudi and Ramya Rajanala and Pothuri Surendra Varma},
title={Avian Species Population Forecaster Using Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={929-935},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013607700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Avian Species Population Forecaster Using Machine Learning
SN - 978-989-758-763-4
AU - Morampudi L.
AU - Rajanala R.
AU - Varma P.
PY - 2025
SP - 929
EP - 935
DO - 10.5220/0013607700004664
PB - SciTePress