Title: Advancements and challenges in bird migration models: a comprehensive survey

Authors: Prajakta Prakash Musale; Shilpa Snehal Sonawani

Addresses: Department of Computer Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, World Peace MIT University, Survey No. 124, Paud Rd., Kothrud, Pune, Maharashtra, 411038, India ' Department of Computer Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, World Peace MIT University, Survey No. 124, Paud Rd., Kothrud, Pune, Maharashtra, 411038, India

Abstract: Bird migration is a critical natural process that supports ecosystem stability and species conservation. This study explores the use of machine learning and deep learning models to predict bird migration patterns, focusing on their application in conservation efforts, habitat preservation, and climate change adaptation. With India's diverse ecosystems providing critical habitats for numerous bird species, predicting migration patterns is key to effective wildlife management. A review of over 50 studies on bird migration prediction highlights methodological approaches, challenges, and observation results, emphasising the need for reliable forecasting models. These models can help reduce risks such as bird strikes in urban development and inform climate change policies. The findings demonstrate the potential of predictive technologies to support global efforts in conserving migratory species and understanding ecosystem dynamics.

Keywords: bird migration prediction; machine learning; deep learning; hybrid model; other model.

DOI: 10.1504/IJGENVI.2026.155260

International Journal of Global Environmental Issues, 2026 Vol.25 No.1, pp.1 - 26

Received: 04 Feb 2025
Accepted: 26 Jan 2026
Published online: 29 Jul 2026 *

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