Descripción del proyecto
Integrar los patrones espaciales en el aprendizaje automático ecológico
En la investigación ecológica, es esencial elaborar mapas espaciales precisos de variables críticas como la biodiversidad y el clima. Los métodos actuales de aprendizaje automático, como los bosques aleatorios, son eficaces, pero a menudo pasan por alto las intrincadas pautas espaciales inherentes a los procesos ecológicos. Esta limitación dificulta nuestra comprensión de los ecosistemas complejos y compromete la precisión de las predicciones. Con el apoyo de las Acciones Marie Skłodowska-Curie, el equipo del proyecto PRISM aborda esta cuestión al integrar y validar las pautas espaciales en modelos de aprendizaje automático. Basado en la geografía, la ecología y la informática, en PRISM se adopta un método de ciencia abierta para difundir ampliamente sus resultados. Se fomenta la colaboración entre investigadores e instituciones, lo que enriquece las competencias y metodologías. En última instancia, PRISM promete mejorar la investigación ecológica al ofrecer predicciones más precisas y una visión más profunda de las pautas espaciales.
Objetivo
Ecological research necessitates the production of spatial maps representing an array of critical variables such as biodiversity, climate, land cover, and soil carbon storage. While current machine learning methods, such as the well-known random forest (RF), have been effective in generating maps for these variables, they often overlook the intricate spatial patterns inherent in ecological processes. The PRISM project seeks to introduce a novel approach that addresses this limitation by integrating and validating spatial patterns within machine learning models. The project will draw upon insights from various fields, including geography, landscape ecology, statistics, and computer science. To ensure the widespread dissemination and accessibility of its findings, PRISM will adopt a comprehensive open science approach, including manuscript publications, the development of open-source software, and the sharing of repositories containing data and code, enabling others to reproduce and build upon the project's results. Through this project, an exchange of knowledge is anticipated between the researcher and the host institution, fostering a collaborative partnership. Under the supervisor's mentorship, the researcher will acquire essential skills in group organization, grant preparation, and research leadership. The researcher will enrich the host institution by creating innovative methods for spatial data analysis, implementing impactful teaching methodologies, and sharing the principles of open science. Ultimately, the PRISM project is poised to fuel the researcher's interdisciplinary growth, positioning him as a valuable asset in both academia and industry. The project's outcomes have the potential to improve how ecological research is conducted, leading to more accurate predictions and a deeper understanding of complex spatial patterns in ecological systems.
Ámbito científico
- natural sciencescomputer and information sciencesdata science
- natural sciencesbiological sciencesecologylandscape ecology
- natural sciencesearth and related environmental sciencessoil sciencesland-based treatment
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinador
48149 MUENSTER
Alemania