Researcher · University of Alicante
I study how spatiotemporal AI systems can make dependable forecasts when sensors, data and deployment conditions are imperfect.
Research perspective
Marc Semper Lloret is an associate lecturer and researcher in the Department of Computer Science and Artificial Intelligence at the University of Alicante, and a member of the Network Data Analysis and Visualisation research group.
His work connects graph learning, environmental observation and robust evaluation. The aim is not only to improve predictive accuracy, but to understand whether the selected model remains a defensible choice under realistic data and deployment uncertainty.
Read the research overviewResearch areas
Four connected lines of work centred on environmental systems, imperfect observations and accountable model choice.
Evaluation and selection methods for forecasting systems that must remain dependable under missing data, sensor failures, spatial misalignment and changing deployment conditions.
Graph neural networks that capture relationships across distributed environmental sensors, cities and global observational grids.
The effect of missingness, observational uncertainty, aggregation and measurement inconsistencies on learned models and the decisions built on them.
AI methods for air quality, atmospheric aerosols, greenhouse-gas concentrations, urban noise and other environmental phenomena.
Selected work
Knowledge-Based Systems, article 116673, 2026
Choosing the best forecasting model from one clean validation set can produce a fragile deployment decision. This work tests whether that decision survives realistic changes to the evaluation reference and turns the diagnosis into an auditable recommendation.
Mathematics, vol. 14, no. 5, article 908, 2026
This study asks whether a graph forecasting model can learn better node representations by training across several related datasets instead of learning every representation from scratch.
International Journal of Environmental Science and Technology, vol. 23, no. 1, article 69, 2026
The work models how atmospheric aerosols evolve across the planet by combining a global graph of observations with temporal patterns at several scales.
Applied Sciences, vol. 15, no. 10, article 5576, 2025
Urban noise has both a temporal rhythm and a spatial structure. The study compares deep-learning approaches that represent those two dimensions explicitly across Madrid.
Journal of Environmental Management, vol. 371, article 122922, 2024
This research tests deep-learning strategies for forecasting global carbon dioxide and methane concentrations six months ahead from satellite and environmental data.
Doctoral thesis
Spatiotemporal modelling with neural networks for forecasting environmental phenomena. Defended at the University of Alicante on 11 December 2025.
Explore the thesisAcademic identity