Nilo Antonio de Souza Sampaio

UERJ, Brazil

Title: Applications of Statistics and Artificial Intelligence in Environmental Research

---

Biography

Nilo holds a bachelor's degree in Chemical Engineering from the Lorena School of Engineering EEL- USP (1994), a master's degree in Mechanical Engineering from the University of Taubaté UNITAU (2005), and a doctorate in Mechanical Engineering from the São Paulo State University Júlio de Mesquita Filho UNESP (2011). He is currently an Associate Professor at the State University of Rio de Janeiro, teaching undergraduate engineering courses, the Master's in Environmental Engineering (PEAMB), and the Doctorate in Environmental Engineering (DEAMB). He is the leader of the Environmental and Climate Data Analysis Research Group: statistical treatment and use of artificial intelligence, registered with CNPq. He has experience in the field of Mathematics and Statistics, with an emphasis on Applications of Statistics and Mathematics in Science, Mathematical Modeling, Optimization and Design of Experiments, Multivariate Statistics, Process Monitoring, and Artificial Intelligence.

Abstract

The growing complexity of environmental problems requires analytical tools capable of dealing with large volumes of data, uncertainties and non-linear patterns. In this context, Statistics and Artificial Intelligence (AI) have played central roles in analysis, prediction and decision-making in environmental research.

Statistics, traditionally used in environmental studies, allows for exploratory data analysis, hypothesis testing and probabilistic modeling. For example, statistical time series analysis has been widely applied to detect trends in climate data such as temperature and precipitation. Statistical models are also essential for making inferences about water quality samples, species distribution and environmental risks. On the other hand, Artificial Intelligence, especially Machine Learning techniques, has complemented Statistics by enabling the processing of large volumes of data with complex patterns. Artificial neural networks, random forests, and support vector machines (SVM) are commonly used in air quality forecasting, deforestation detection, and land use classification from satellite images. The synergy between statistics and AI also allows for the development of hybrid models, combining statistical inference with the predictive power of AI algorithms, expanding.