Descripción del proyecto
Métodos avanzados de regresión revelan los factores del cambio climático en los datos de observación de la Tierra
Los modelos matemáticos y la creciente potencia de cálculo han sido esenciales para comprender sistemas dinámicos complejos que implican conjuntos de datos tremendamente grandes que ocultan muchas interrelaciones. A partir de los datos de los satélites de observación de la Tierra, proporcionan información sobre el cambio climático. Estos cambios son cada vez más rápidos y difíciles de comprender y predecir. Las nuevas misiones satelitales aportarán datos aún más complejos, heterogéneos, estructurados y de múltiples fuentes, que requerirán mejores métodos de inferencia estadística para ser plenamente explotados. El equipo del proyecto SEDAL, financiado por el Consejo Europeo de Investigación, desarrolló métodos avanzados de regresión para mejorar la eficacia, la precisión de las predicciones y las incertidumbres, codificar los parámetros físicos y permitirnos descubrir factores esenciales ocultos y factores de confusión.
Objetivo
SEDAL is an interdisciplinary project that aims to develop novel statistical learning methods to analyze Earth Observation (EO) satellite data. In the last decade, machine learning models have helped to monitor land, oceans, and atmosphere through the analysis and estimation of climate and biophysical parameters. Current approaches, however, cannot deal efficiently with the particular characteristics of remote sensing data. In the coming few years, this problem will largely increase: several satellite missions, such as the operational EU Copernicus Sentinels, will be launched, and we will face the urgent need to process and understand huge amounts of complex, heterogeneous, multisource, and structured data to monitor the rapid changes already occurring in our Planet.
SEDAL aims to develop the next generation of statistical inference methods for EO data analysis. We will develop advanced regression methods to improve efficiency, prediction accuracy and uncertainties, encode physical knowledge about the problem, and attain self-explanatory models learned from empirical data. Even more importantly, we will learn graphical causal models to explain the potentially complex interactions between key observed variables, and discover hidden essential drivers and confounding factors. This project will thus aboard the fundamental problem of moving from correlation to dependence and then to causation through EO data analysis. The theoretical developments will be guided by the challenging problems of estimating biophysical parameters and learning causal relations at both local and global planetary scales.
The long-term vision of SEDAL is tied to open new frontiers and foster research towards algorithms capable of discovering knowledge from EO data, a stepping stone before the more ambitious far-end goal of machine reasoning of anthropogenic climate change.
Ámbito científico
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
46010 Valencia
España