Machine Learning and Big Data Analytics for Sustainable Food-Energy-Water Nexus.

Investigación

Food, energy, and water (FEW) are the physical resources and natural systems in the earth for humanity surviving, sustainable food, energy and water have been seriously considered as the United Nations' Sustainable Development Goals 2030. In the last decade, huge amount of FEW data haven been accumulated thanks to the technology advances. Additionally, the FEW data are big, spatial, and dynamic, presenting the characteristics of Big Data such as Velocity, Variety, Veracity, Vulnerability, Viscosity, Vararity, etc. Data scientists have noticed the big opportunities in FEW nexus challenges. In this project we focus on solving real problems in the FEW nexus using data science, such as wind power energy production prediction, land irrigation efficiency analysis, water quality monitoring, plant disease detection, etc. Our principal goal is to investigate machine learning and Big Data Analytics techniques for sustainable FEW systems, such as spatio-temporal data, high dimensionality, imbalance data, complexity of heterogeneous data, data fusion, etc. Such application-oriented research can greatly promote artificial intelligence (AI) development in Mexico, that will benefit to academy, industry, and the government. Along with the application-oriented research, we want to create a FEW data repository to facilitate academy people doing theoretical work. The destinating objective is to form a multidisciplinary FEW system research team in Mexico.

Proyecto

Clave:# CF-2023-I-2614
Vigencia: Julio 2023 - Junio 2026.
Participante(s): Dra. Xiaoou Li.
Empresa/Dependencia solicitante: Ciencia de Frontera.
Tipo de proyecto: Investigación y Desarrollo.