Anatomy of the fetal membranes: Insights from spinning disk confocal microscopy. Archives of Gynecology and Obstetrics, (309)5:1919-1923, Springer Berlin Heidelberg, 2024. [PUMA: zno]
Rapid prototyping of 3D biochips for cell motility studies using two-photon polymerization. Frontiers in bioengineering and biotechnology, (9):664094, Frontiers Media SA, 2021. [PUMA: zno]
Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil. Journal of Water Resources Planning and Management, (147)1:05020026, American Society of Civil Engineers, 2021. [PUMA: zno]
Application of stochastic dual dynamic programming in operation optimization of the Jaguaribe-Metropolitano reservoirs system. 2019. [PUMA: zno]
Water demand modeling using machine learning techniques. 2019. [PUMA: zno]
Avaliação do uso de cisternas como medida compensatória para atenuação de picos de cheia na Bacia do Pajeú utilizando o SWMM. 2017. [PUMA: zno]
Integrated model of capacity expansion and operation of water supply systems including non-conventional water sources. 2019. [PUMA: zno]
Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning. Environmental Science and Pollution Research, (29)49:74967-74982, Springer Berlin Heidelberg, 2022. [PUMA: zno]
Vulnerability index to COVID-19: Fortaleza, Brazil study case. Engenharia Sanitaria e Ambiental, (26):731-739, Associação Brasileira de Engenharia Sanitária e Ambiental-ABES, 2021. [PUMA: zno]
Modelagem dinâmica espacial aplicada à previsão da demanda hídrica. ABRHidro-Associação Brasileira de Recursos Hidrícos, http://www. abrhidro. org. br/xxivsbrh, 2021. [PUMA: zno]
Hydrological risk of dam failure under climate change. RBRH, (27):e19, Associação Brasileira de Recursos Hídricos, 2022. [PUMA: zno]
Identification of correlation between residential demand and average income using pool regression model. 2019. [PUMA: zno]
Mapeamento da produção científica internacional sobre previsão da demanda hídrica urbana. ABRHidro-Associação Brasileira de Recursos Hidrícos, http://www. abrhidro. org. br/xxivsbrh, 2021. [PUMA: zno]
Use of Machine Learning in Evaluation of Drought Perception in Irrigated Agriculture: The Case of an Irrigated Perimeter in Brazil. Water, (12)6:1546, Multidisciplinary Digital Publishing Institute, 2020. [PUMA: zno]
Análise do desempenho de valas de infiltração para controle pluvial em cenários de mudanças climáticas: estudo de caso Fortaleza (CE). Revista DAE, (72)245:01-12, 2024. [PUMA: zno]
Detecção de secas e visualização de padrões climáticos com aprendizado de máquina. ABRHidro-Associação Brasileira de Recursos Hidrícos, http://www. abrhidro. org. br/xxivsbrh, 2021. [PUMA: zno]
Diversification of urban water supply: An assessment of social costs and water production costs. Water Policy, (24)6:980-997, IWA Publishing, 2022. [PUMA: zno]
Exploring the relationship between climate indices and hydrological time series using a machine learning approach. 2019. [PUMA: zno]
Integrated proactive drought management in hydrosystems and cities: building a nine-step participatory planning methodology. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, (115)3:2179-2204, Springer, 2023. [PUMA: zno]
Machine Learning for Water Resources Management. 2023. [PUMA: zno]