Expert systems and environmental monitoring

The transition from empirical assessments to scientifically based methods of making environmentally sound decisions lies through the creation of expert environmental monitoring systems focused on assessing and predicting the state of the environment under anthropogenic impact. The natural environment (NSE) is considered as an open dynamic system. The phase space of a dynamical system is characterized by the state space considered as a point in some generalized coordinates. Each state of the system, in other words, its movement, corresponds to a certain trajectory of movement of the display point in the phase space. For ontology models with their poor mathematical structure (there is no topological or differentiable structure), the issues of roughness (structural stability) of systems are solved by creating discrete models. Therefore, in order to construct the ontology of the OPS, it is necessary to use data that relate to a certain period of time and a certain territory, obtained as a result of generalization over a set of observed parameters.
Various models of knowledge bases based on environmental and economic information and focused on the environmental assessment of the state of the environmental protection system are considered. Ontologies were developed using the methodology for constructing knowledge base in the "KARKAS" system.
 

An expert system (ES) is a computer system that allows, on the basis of a knowledge base compiled by experts from a specific subject area, to solve a problem with the help of a logical conclusion.

An expert system (ES) is a computer system that allows, on the basis of a knowledge base compiled by experts from a specific subject area, to solve a problem with the help of a logical conclusion.
The main components of the ES: knowledge base; fact base; knowledge base editor; inference engine; explanation subsystem; user interfaces and knowledge engineer.
A subject area (SrA) is a part of the real world that is modeled with the help of expert knowledge.
The knowledge base (KB) is a collection of formalized knowledge about the subject area.
The Fact Base (FB) is data on the processes and phenomena of the subject area.
The KB editor allows you to enter and correct formalized knowledge.
An inference engine is a program code that implements inference based on knowledge of knowledge base.
The explanation subsystem is a program code that allows you to trace the logical conclusion on the application of knowledge from the knowledge base.
ES is created as a result of the work of an expert and a knowledge engineer based on the tools of AI systems.
A knowledge engineer (cognitive scientist, analyst) extracts knowledge from knowledge sources (expert, Internet content, and so on). The result of his work is a formalized KB model, during the creation of which the SbA is analyzed, knowledge is extracted, and knowledge is structured. In this case, a model is understood as a set of descriptions of the SbA entities and their relationships.
At the present stage of AI development, the term ontology is used to formalize a knowledge area using a conceptual scheme, as a form of representing knowledge about the real world in a computer format.
The use of ES can significantly enhance the intellectual potential of a person and help a specialist in solving many professional problems.