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This paper presents the research results related to the development of an intelligent system for monitoring and assessing the state of natural systems (PROSTOR), which was tested in the area from the city of Velikiy Ustyug to the city of Kotlas on the Northern Dvina River. It is one of the most vulnerable places in Russia to spring snow-melt and ice-jams induced floods.The proposed automated flood forecasting technology is based on the concept of a multi-model description of complex natural objects implementing a mechanism of the selection and adaptation of parameters of the most adequate model for each specific situation. The computational core of PROSTOR is the two-dimensional hydrodynamic model STREAM_2D and its newer version STREAM_2D_CUDA based on the numerical solution of the shallow water equations with discontinuous bottom. Additional hydraulic resistance due to the ice roughness and decrease in the flow cross-section due to ice-caused congestion were taken into account for modeling the ice-jams water levels. The forecasting capabilities of the system are secured by the prediction of water levels at the gauging stations located upstream from Velikiy Ustyug basing either on neural networks, or by means of linking with the runoff formation model ECOMAG and using prognostic meteorological information.The system was built with the use of a service-oriented architecture, that provides flexible interaction between software modules, implementing hydrodynamic and hydrological models; modules of collecting and processing of heterogeneous data, including data from gauging stations and remote sensing data; control modules, etc. All system components are realized as web services and can be geographically distributed and localized in various organizations, cities and countries. All results of the system implementation, including the results of flooded zones calculations, flow parameters there, as well as satellite images are available via the geoportal.Models parameters were justified on the base of numerical experiments and simulations of the floods of 1980-2016 period, including more than 18 significant cases of ice-jamming. Grouping of model parameters according to the height of the ice-jam induced water levels suggested for the implementation of the hydrodynamic model incorporated into intelligent information system of river floods monitoring. Operational flood forecasting mode of the system was tested during 2017 – 2019 years under support of Russian Science Foundation project № 17-11-01254.

The paper proposes a method and a multi-model complex for managing computations, which make it possible to increase the efficiency of production processes at existing and prospective industrial enterprises due to the optimal (rational) functioning of their information systems. The features of computational processes and architectures of information systems of modern enterprises based on the concept of Industry 4.0 are considered. A brief description of the software package is given, in which the task of structural-functional synthesis of the structure of an enterprise information system, as well as the task of building an operational schedule for its work, are simultaneously solved in an automated (automatic) mode.

An important factor in the recognition of magnetic resonance images is not only the accuracy, but also the speed of the segmentation procedure. In some cases, the speed of the procedure is more important than the accuracy and the choice is made in favor of a less accurate, but faster procedure. This means that the segmentation method must be fully adaptive to different image models, that reduces its accuracy. These requirements are satisfied by developed hyper-heuristical particle swarm method for image segmentation. The main idea of the proposed hyper-heuristical method is the application of several heuristics, each of which has its strengths and weaknesses, and then their use depending on the current state of the solution. Hyper-heuristical particle swarm segmentation method is a management system, in the subordination of which there are three bioinspired heuristics: PSO-K-means, Modified Exponential PSO, Elitist Exponential PSO. Developed hyper-heuristical method was tested using the Ossirix benchmark with magnetic-resonance images (MRI) with various nature and different quality. The results of method’s work and a comparison with competing segmentation methods are presented in the form of an accuracy chart and a time table of segmentation methods.

The paper presents the state-of-the-art in flood forecasting and simulation applied to a river flood analysis and risk prediction. Different water flow forecasting and river simulation models and systems are analysed. An advanced river flood monitoring, modelling and forecasting approach is introduced. It extends the traditional approach based on modelling river physical processes by integration of different types of models and technologies such as input data clustering and filtering, digital maps of a relief and river terrain, data crowdsourcing, heterogeneous data processing, hydrological models for time scale modelling water flows and geo-simulation, inundation visualisation and duly warning on flooding. A case study on river flow forecasting and simulation for river flood risk analysis and management is given.

The chapter presents a holistic system that implements an advanced river flood modeling and forecasting approach. This approach extends traditional methods based on separate satellite monitoring or river physical processes modeling, by integration of different technologies such as satellite and in situ data processing, input data clustering and filtering, digital mapping of river valleys relief, data crowdsourcing, hydrodynamic modeling, inundation visualization, and also duly warning of stakeholders.

Elements of the methodological basis of the theory of monitoring automated (computer-aided) systems structure-dynamics control are proposed in the paper. This theory can be widely used in practice. It has an interdisciplinary basis provided by the classic control theory, operations research, artificial intelligence, systems theory and systems analysis. The proposed approaches were implemented in software prototypes. The software prototypes were developed to simulate control processes for space-facilities control system (SF CS). The unified description of various control processes allows synthesizing both technical and functional structures of SF CS simultaneously. The presented multiple-model complex, as compared with known analogues, has several advantages. It simplifies decision-making in SF CS structure dynamics management, for it allows seeking for alternatives in finite dimensional spaces rather than in discrete ones. The complex permits to reduce dimensionality of SF CS structure-functional synthesis problems in a real-time operation mode.

In this paper a new approach to the creation of short-term forecasting systems of river flooding is being further developed. It provides highly accurate forecasting results due to operative obtaining and integrated processing of the remote sensing and ground-based water flow data in real time. Forecasting of flood areas and depths is performed on a time interval of 12–48 h to be able to perform the necessary steps to alert and evacuate the population. Forecast results are available as web services. The proposed system extends the traditional separate methods based on satellite monitoring or modeling of a river’s physical processes, by using an interdisciplinary approach, integration of different models and technologies, and through intelligent choice of the most suitable models for a flood forecasting.

Recent natural disasters and major technological incidents as well as intensification of human activities prove the strategic necessity for the effective use of results of natural and technological object monitoring and its integration within the national economic processes. This book presents main results achieved within the research project 2.1/ EL RI‑184/2011/14 “Integrated Intelligent Platform for Monitoring the Cross-Border Natural-Technological Systems” (IN FROM) as part of “Estonia – Latvia – Russia Cross- Border Cooperation Programme within European Neighbourhood and Partnership Instrument 2007–2013”, from both theoretical and implementation perspectives. The project aims to improve the integrated monitoring and control of cross-border complex systems, which contain natural, technological and social elements, in normal and emergency situations based on heterogeneous data received from space and ground-based information sources. Nowadays, monitoring and control methods are applicable only for specific Natural Technological Systems (NTSs). As a result, statistical information about existing systems is not well coordinated. This drawback becomes more evident in emergency situations, when effective decisions must be taken within a short period of time while different information flows have to be analysed. Monitoring information regarding incidents and disasters is received typically from different facilities (e. g., biometric systems, aerospace systems, etc.), and, therefore, it is heterogeneous in nature (e. g., electrical signals, audio and video information, text, etc.). Since modern NTSs are very complex and multi-functional objects, their monitoring and control should be performed in conditions of large-scale heterogeneous datasets. Nowadays, the monitoring and control processes of NTSs are still not completely automated. The IN FROM project addresses the problem of integrated monitoring and control of cross-border natural-technological systems in normal and emergency situations, based on the analysis of heterogeneous data received from both space and ground-based facilities. The overall objective of the project is to develop a universal common intelligent platform for unifying efforts of specialists from Russia and Latvia to protect the environment and natural resources, based on the integrated space-ground monitoring. The project results achieved provide a unified approach to the integrated monitoring and control of complex systems based on the analysis of heterogeneous data received from space and ground-based facilities, by using different types of models (i. e., analytical, algorithmic, mixed) to model behaviour of these systems. In order to select and develop an appropriate model, techniques for estimation of the model quality and its adjustment to a real application are enrolled. The book is recommended to the professionals responsible for monitoring of complex natural and technological objects and researchers working in this area. 12 The book contains a list of authors, a preface, four chapters as well as lists of abbreviations, figures and tables, and a summary. Each chapter is provided with references. The preface gives a short overview of the INFROM project objectives and briefly describes contents of the book. Chapter 1 presents the state of the art in the development of computationally efficient and user-centred geospatial monitoring, analysis and modelling framework that allows processing and integration of remotely sensed data at different spatial and time scales, as well as modelling and simulation for assessment of possible future development scenarios of the monitored and analysed natural and technological objects. Chapter 2 provides an insight into the advanced innovative information technologies for integrated space and ground-based monitoring of complex systems containing natural, technological and social elements. Chapter 3 describes an integrated distributed network framework introduced in the project and the developed infrastructures. It also presents support tools for intelligent space and ground-based monitoring of river floods, integrated forest monitoring, including forest taxation and evaluating forest fire safety conditions and tools for information processing of water pollution. Demonstration cases to prove functionality of the proposed technology platform, developed information processing and decision support tools are introduced in Chapter 4. These demonstration cases provide simulation-based Daugava River flood forecasting; integrated forest monitoring at Madona municipality and in Pskov region as well as monitoring of oil pollution in the Gulf of Finland and the overgrowing of Lubans Lake. Finally, concluding remarks are provided in the summary. We would like to express our gratitude to all project participants contributed to the preparation of this book as well as to representatives of the target groups actively participated in the implementation of the IN FROM project. We are also pleased to acknowledge great support provided by colleagues from the institutions, involved into project implementation, in particular, project partners – Riga Technical University (Latvia) and St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (Russia), as well as project associate partners in Latvia (Latvian Transport Development and Education Association, and Diplomatic Economic Club) and Russia (Committee on IT and Communications, Government of the City of St. Petersburg). Our special thanks for the permanent support in searching for new approaches, developing new methods, algorithms and software tools in order to implement them, and finally for demonstrating efficiency of the developed methods and tools in particular demonstration cases go to the project Advisory Board and personally to its Chair Dr. Jānis Lelis. We would also like to acknowledge the valuable and professional support of the Joint Technical Secretariat of Estonia – Latvia – Russia Cross-Border Cooperation Programme officers during the project implementation and preparation of this book.

Monitoring of different natural and technological objects is essential in the modern world to ensure a safe and comfortable people’s lifestyle; it helps to understand how the planet and its climate change, what role is played by anthropogenic and technological factors in these changes. Different methods and decision-making models are used in the monitoring and control of natural and technological objects; this paper provides an overview of the commonly used models of ground-space monitoring of natural and technological objects, which have been analysed in the framework of the project INFROM.