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Sea Turtle Trajectories Prediction via Long Short-Term Memory and Kalman-Filter

  • Carlos A. Rovetto R.
    Facultad de Ingeniería en Sistemas Computacionales, Universidad Tecnológica de Panamá, Panamá carlos.rovetto[at]utp.ac.pa
  • Eric E. Flores
    Coiba Scientific Station (COIBA AIP), Panamá, Panamá
  • Kexy Rodriguez
  • Ivonne Nuñez
    CDS Research Group, Institute of Computer Science, University of Bern, 3012 Bern, Switzerland
  • Andrzej Smolarz
    Lublin University of Technology, Department of Computer and Electrical Engineering, Poland
  • Dimas Concepción
    Escuela de Innovación Digital, Instituto Técnico Superior Especializado, Panamá, Panamá
  • Elia E. Cano
https://doi.org/10.14201/adcaij.33385

Abstract

Tracking sea turtle migration is hindered by noisy and incomplete geolocation data, as well as irregular sensor transmission. These limitations make it challenging to model trajectories and accurately interpret ecological patterns. This study presents a predictive framework for modelling the trajectories of green turtles (Chelonia mydas) using satellite telemetry and artificial intelligence techniques. Georeferenced data from SPOT-375B tags were pre-processed to address noise, data gaps, and spatial anomalies. A Long Short-Term Memory (LSTM) neural network was trained with normalized time series data to forecast future positions, capturing the temporal dependencies of turtle movement. A Kalman filter was applied post-prediction to enhance trajectory continuity and reduce uncertainty through recursive state estimation. Experimental results show that the approach yields an average MAE of 0.0986, MSE of 0.0307, and RMSE of 0.1288, and reduces mean prediction error by 43.75 % relative to a recurrent neural network (RNN) baseline while requiring ~36 % of its CPU time. This integrated pipeline enhances the reliability of wildlife trajectory forecasting and provides a scalable solution for ecological tracking under uncertain detection conditions, facilitating a deeper understanding of species behavior and more effective conservation strategies.

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Rovetto R., C. A., Flores, E. E., Rodriguez, K., Nuñez, I., Smolarz, A., Concepción, D., & Cano, E. E. (2025). Sea Turtle Trajectories Prediction via Long Short-Term Memory and Kalman-Filter. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e33385. https://doi.org/10.14201/adcaij.33385

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