Maryna Zharikova

Bio: Maryna Zharikova is an academic researcher from Kherson National Technical University. The author has contributed to research in topics: Decision support system & Remote sensing (archaeology). The author has an hindex of 4, co-authored 5 publications receiving 55 citations.

Forest Fire-Fighting Monitoring System Based on UAV Team and Remote Sensing

Abstract: This work presents a monitoring system for tactical forest fire-fighting operations based on a team of unmanned aerial vehicles and remote sensing techniques. Functions and missions of the system, as well as its architecture, are considered. Image processing and remote sensing algorithms are presented, a way for data integration into a fire-spreading model in a real-time forest fire response decision support system is proposed. The combination of automatic monitoring and remote sensing techniques with an approximate fire-spreading model can provide required credibility and efficiency of fire prediction and response.

Threat Assessment Method for Intelligent Disaster Decision Support System

Abstract: The qualitative danger and threat assessment method based on the principle of the maximal allowable limits is proposed for the intelligent disaster decision support system. The proposed method uses the rough set based plausible disaster spreading model and the formal model of the territorial system, which allows us to consider the dynamics of natural disaster spreading discretely at the level of individual cells of the grid and describes disaster dynamics as moving a vague contour presented as a boundary region of a rough set on the certain terrain. Both the plausible disaster spreading model and the qualitative danger and threat assessment method were developed for wildfires as the most common class of natural disasters, and can be suitable for solving decision support tasks for protection against other natural disasters without loss of clarity and justification for the decision-maker.

Forest Fire Monitoring System Based on UAV Team, Remote Sensing, and Image Processing

Abstract: This work presents the fire monitoring and detecting system for tactical forest fire-fighting operations based on a team of unmanned aerial vehicles, remote sensing, and image processing. The idea of such a system and its general parameters and possibilities are described. Functions and missions of the system, as well as its architecture, are considered. The image processing and remote sensing algorithms are presented, a way for data integration into a real-time DSS is proposed. The results of experimental research of the prototype system are presented. The combination of multi-UAV-based automatic monitoring, remote sensing and image processing techniques provides required credibility and efficiency of the fire detection.

Plausible Event-Tree Networks for Knowledge Representation in Real-Time GIS-Based Decision Support Systems.

Abstract: Event-based knowledge representation models providing sufficient detail in space and time are often necessary for real-time GIS-based decision support systems. The paper is devoted to developing such a model based on a plausible event tree network, which is built over a spatial model of a terrain discretized with a grid of uniform-sized cells. Each event has not only time reference, but also spatial reference, and describes a transition of the cell from one state to another. It combines different kinds of likelihood assessments (probability, fuzzy, or rough) using various plausibility models. The paper describes the event tree network-based knowledge representation, which can be used to describe a multitude of interacting processes on the terrain. An experiment based on real data describing a forest fire cascade has been conducted and has confirmed the validity and usability of the proposed model for the considered class of GIS-based decision support systems.

Approximate model of spatially distributed Markov process for GIS-based decision support system

Abstract: This work presents an approximate model of spatially distributed Markov processes for the GIS-based real-time disaster Decision Support System. The proposed model uses possibility measures based on rough or fuzzy sets approach. The transition distribution of vague Markov jump-type process can be determined using the possibility values. The dynamics of the processes is represented as the motion of the boundary region of a topological space, where regions and areas can be blurred using fuzzy or rough sets of cells. The implementation of the model reduces the computational complexity and provides the acceptable performance of the decision support system.

A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

Abstract: Forest fires are one of the major environmental concerns, each year millions of hectares are destroyed over the world, causing economic and ecological damage as well as human lives. Thus, predicting such an environmental issue becomes a critical concern to mitigate this threat. Several technologies and new methods have been proposed to predict and detect forest fires. The trend is toward the integration of artificial intelligence to automate the prediction and detection of fire occurrence. This paper presents a comprehensive survey of the machine learning algorithms based forest fires prediction and detection systems. First, a brief introduction to the forest fire concern is given. Then, various methods and systems in forest fires prediction and detection systems are reviewed. Besides works that reported fire prediction and detection systems, studies that assessed the factors influencing the fire occurrence and risk are discussed. The main issues and outcomes within each study are presented and discussed.

Forest Fire-Fighting Monitoring System Based on UAV Team and Remote Sensing

Abstract: This work presents a monitoring system for tactical forest fire-fighting operations based on a team of unmanned aerial vehicles and remote sensing techniques. Functions and missions of the system, as well as its architecture, are considered. Image processing and remote sensing algorithms are presented, a way for data integration into a fire-spreading model in a real-time forest fire response decision support system is proposed. The combination of automatic monitoring and remote sensing techniques with an approximate fire-spreading model can provide required credibility and efficiency of fire prediction and response.

A review on drones controlled in real-time

Abstract: This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

Abstract: The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.