In the context of the consultancy and research activities, in addition to commercial products and GIS applications, use is also made of specialized modelling tools developed by EMVIS for addressing specific issues, such as the determination of point and non-point pollution loads at river basin level, the assessment of impacts to water bodies and the hydraulic performance and process simulation of various treatment systems for liquid and solid wastes.
Exploiting environmental information
Ecoinformatics, the science of information in ecology and environmental science, is emerging to deal with the interpretation of data relevant to ecological and broader environmental processes, especially problems posed by global climate and land use change. Exploiting the complex and huge amount of environmental information that is being generated every day is a challenge that requires novel and integrated approaches at the interface of ecology, mathematics, statistics, computer science and engineering.
The environmental modelling team of EMVIS has a large experience in GIS mapping and analysis systems and is also familiar with advanced, state of the art commercial software which are essential for improving the understanding of complex ecological systems. Furthermore EMVIS scientists have extended experience in major programming stacks (Microsoft.NET, C#, FORTRAN) and database systems (MySQL, PostgreSQL). These technologies can play an important role in developing and testing detailed customized models that describe environmental processes/physics of real-world systems. In SPACE-O, a recent H2020 collaborative project coordinated by EMVIS, advanced techniques are used to integrate Earth Observations and in-situ monitoring systems with state of the art hydrological and water quality models, in order to detect, evaluate and predict eutrophication phenomena in reservoirs used for drinking water purposes.
The past years machine learning techniques have become very popular mainly due to advances in remote sensing and smart metering as well as the increase of computing power and storage capacity. Nowadays Water Utilities are collecting huge amounts of operational data that may be used to optimize the operational rules and improve overall system efficiency. Our modelling team has significant experience in developing and training advanced machine learning models that describe the performance of various process stages in Water Treatment Plants, aiming at cost efficient operations and sustainable performance.