Application of a remote sensing data processing method for assessment ice cohesion in the Arctic navigation

• Published in: IOP Conference Series: Earth and Environmental Science
• Main Authors: Sidorenko, A, Stepanov, S, Petrov, Y, Martyn, I
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2020
• Subjects: Arctic
• Online Access: http://dx.doi.org/10.1088/1755-1315/539/1/012128; https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012128/pdf; https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012128

Abstract Operational correct predictive information about an ice situation in the Arctic region is an important component in making strategic management decisions and planning the route of vessels, in this regard, the scientific work examines the application of a remote sensing data processing method for assessment ice cohesion in the Arctic navigation. The article presents a generalized model for assessment the state of a stochastic process using the Kalman filter in the environment of mathematical modeling Matlab. The algorithm of behavior of mathematical tools of the Kalman filter is considered. During the research the algorithm of filtration was corrected by adding recursion to the filtering algorithm. To simulate the method of processing spatially distributed data using the Kalman filter and to analyze the convergence of predictive data with actual data, we used hydrometeorological data for the sampling period from 01.01.2016 to 12.31.2018 according to remote sensing data. The obtained results illustrate the correctness of the hypothesis and show the possibility of using the described approach to solve problems of this kind. The proposed algorithm confirms the possibility of using combined prediction methods and causes perspectives of the development prospects of the research topic. Results of scientific activity allow to give the assessment to relevance of using such a prediction method. Based on the conducted research, we can conclude that, in the presence of sufficient and reliable data, the proposed method is suitable for further deeper consideration. Further development of scientific research is directed to improvement of quality of methods of prediction which gradual growth, will undoubtedly lead to expansion of the period of the useful predict.