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Artificial intelligence in environmental management

The emergence of Artificial Intelligence (AI) techniques in various sectors (gaming, finance, industry, science, etc.) has opened up the possibility of finding solutions to complex problems that until now have been difficult to resolve.  Furthermore, the introduction of real-time data acquisition technologies via a wide range of sensors (IoT) enables ever more precise diagnostics but generates a large quantity of data that then needs to be analysed using powerful computer software.

AI techniques (Deep machine learning, Deep reinforcement learning, etc.), can recognise patterns in a seemingly chaotic mass of information and help to make decisions not only in the diagnosis of problems but also in their resolution.  Their power lies both in being able to manage and interpret a large quantity of data, and also in combining and linking structured (numerics, logic, etc.) and unstructured (maps, images) information.

Although their use is still new in the environmental sector, the Talantia team is developing methodologies for the resolution of complex problems in collaboration with technology partners.

The bias seen in traditional interpretation can be reduced by managing diverse hypotheses in parallel during the modelling phases and trying out multiple possibilities.  The process is completed by validating the hypotheses and therefore optimising decision-making in uncertain situations.

In the world of natural environment modelling and in particular the contamination of soil and groundwater, atmosphere, surface water etc., this can be a powerful tool in contrasting predictive hypotheses from traditional methodologies.  For example:

  • Applying machine learning to the resolution of differential equations in simulation models
  • in the joint management of various types of environmental and industrial operation data (photographs, maps, non-stop logs, lithology, etc.)

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