Trabalhos completos em revistas científicas com revisão por pares:

• Soares et al. (2025) ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed. Journal of Hydrology, v. 652, p. 132674

• Duque et al (2024) Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin. CHAOS, v. 34, n. 5, p. 053132 

• Soares et al. (2024) Uncertainty propagation analysis for distributed hydrological forecasting using a neural network. Transactions in GIS, v. 1

• Silva et al. (2024)  ShpStreetGraph: From spatial relations of streets to graph representations. Accepted to Software Impacts

Artigos completos submetidos para publicação (preprints):

• Santos et al. (2024) Machine Learning-based Hydrological Models for Flash Floods: A Systematic Literature Review. DOI: https://doi.org/10.31223/X5C699

Além disso o projeto tem publicações em congressos: em 7 eventos nacionais, e 4 eventos internacionais, com destaque para:

• Arturo & Santos (2024) Proposal and Analysis of a Hydrologically Oriented Street Graph  - agraciado com o prêmio de melhor trabalho na área de Física Ambiental no Encontro de Outono da Sociedade Brasileira de Física - Florianópolis, 2024

• Santos & Bacelar (2024) Machine learning hydrological models for flash floods in Brazil - Reunión sobre Sistemas de Alerta Temprana para Inundaciones Repentinas (PREVENIR) - Buenos Aires (Argentina), 2024

• Duque et al. (2025) Dynamical Modeling of Floods Using Surface Water Level Time Series - 11th International Conference on Time Series and Forecasting, Canaria, Spain, 2025