Descrizione del progetto
Integrazione di modelli spaziali nell’apprendimento automatico ecologico
Nella ricerca ecologica è essenziale produrre mappe spaziali accurate di variabili critiche come la biodiversità e il clima. Gli attuali metodi di apprendimento automatico, come la foresta casuale, sono efficaci ma spesso trascurano gli intricati schemi spaziali insiti nei processi ecologici. Questa limitazione ostacola la comprensione degli ecosistemi complessi e compromette l’accuratezza delle previsioni. Con il sostegno del programma di azioni Marie Skłodowska-Curie, il progetto PRISM affronta questo problema integrando e validando i modelli spaziali all’interno dei modelli di apprendimento automatico. Attingendo dalla geografia, dall’ecologia e dall’informatica, PRISM adotta un approccio di scienza aperta per diffondere ampiamente i risultati. Favorisce la collaborazione tra ricercatori e istituzioni, arricchendo competenze e metodologie. In definitiva, PRISM promette di migliorare la ricerca ecologica, offrendo previsioni più precise e approfondimenti sui modelli spaziali.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Meccanismo di finanziamento
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinatore
48149 MUENSTER
Germania