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

Carlos A. Rovetto R.; Eric E. Flores; Kexy Rodriguez; Ivonne Nuñez; Andrzej Smolarz; Dimas Concepción; Elia E. Cano

doi:10.14201/adcaij.33385

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

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How to Cite

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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Editorial dates

Submitted:

01-06-2025

Acceptance:

16-12-2025

Published:

31-12-2025

Issue

Vol. 14 (2025)

Section

Articles

Keywords

sea turtle tracking
Chelonia mydas
Time series prediction
Kalman filter
geospatial analysis
biotelemetry
machine learning

Supporting agencies

We would like to thank the Moreno family in Arenas, Veraguas Province, for granting us access to El Gato Beach, and Joelbin De la Cruz, Daniel Rivas, Bernardo Peña, Wilfredo Poveda, and Adrian Rivas for their assistance during the fieldwork and satellite tag installation. Additionally, we thank Jeffrey Seminoff from NOAA for his advice and suggestions regarding materials and the satellite tag installation procedure. We also express our gratitude to the National Research System of Panama (SNI) and to the Research Group in Engineering Technologies Applied to Society (GIITAS) for their support and collaboration in the development of this research project. The satellite tagging and monitoring program was part of a project funded by the Secretariat of Science, Technology, and Innovation of Panama (SENACYT) under Contract No. 191-2022. This work was conducted under Scientific Permit No. ARB-077-2022 was issued by the Ministry of Environment of Panama.

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