This paper reviews the most important information fusion data-driven algorithms based on
Machine Learning (ML) techniques for problems in Earth observation. Nowadays we …

Abstract Machine learning and artificial intelligence techniques have an ever-increasing
presence and impact on a wide-variety of research and commercial fields. Disappointed by …

Modeling is essential to characterize and explore complex societal and environmental
issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling …

The evidence base in environmental sciences is increasing steadily. Environmental
researchers have been challenged to handle massive volumes of data to support more …

The habitats of marine life, characteristics of species, and the diverse mix of maritime
industries around these habitats are of interest to many researchers, authorities, and …

As one of the earth's key ecosystems, rivers have been intensively studied and modelled
through the application of machine learning (ML). With the amount of large data available …

The use of decision support systems (DSS) allows integrating all the issues related with
sustainable development in view of providing a useful support to solve multi-scenario …

Anomaly detection is the process of identifying unexpected data samples in datasets.
Automated anomaly detection is either performed using supervised machine learning …

Data Mining (DM) is a fundamental component of the Data Science process. Over recent
years a huge library of DM algorithms has been developed to tackle a variety of problems in …

Wastewater treatment plants (WWTPs) are sustainable solutions to water scarcity. As initial
conditions offered to WWTPs, influent conditions (ICs) affect treatment units states, ongoing …