Using Systems of Parallel and Distributed Data Processing to Build Hydrological Models Based on Remote Sensing Data

Abstract

Abstract. The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.

Models of Optimal Operating Modes of the Water-Economic Complex on the Basis of Hydro Resource Price Evaluation

“…For efficient electricity generation at HPP, two main factors are necessary: a guaranteed supply of water during a year and possibly large river falls (canyon-like relief types are favorable for hydraulic construction) [3,4]. Methodologies for solving optimization problems in the case of HPP cascades are significantly complicated and not fully studied [5][6][7][8][9][10][11][12]. The reason for this was the absence in the 1970s-1980s of the necessary powerful automation tools required for solving such problems [13][14][15][16][17][18].…”