Machine learning methods for modelling and analysis of time series signals in geoinformatics

TL;DR

This paper compares deep learning architectures for modeling time series signals in geoinformatics, focusing on ionospheric TEC modeling for GNSS and energy disaggregation for energy efficiency, demonstrating their superior performance over existing methods.

Contribution

It provides a comprehensive comparative analysis of deep learning models applied to two key geoinformatics problems, highlighting their effectiveness and potential for cross-disciplinary applications.

Findings

Deep learning models outperform traditional methods in TEC modeling.

DL architectures achieve higher accuracy in energy disaggregation.

Experimental results confirm the superiority of proposed methods.

Abstract

In this dissertation is provided a comparative analysis that evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications. Two main fruitful research fields are discussed here which were strategically chosen in order to address current cross disciplinary research priorities attracting the interest of geodetic community. The first problem is related to ionospheric Total Electron Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications. Reliable and fast knowledge about ionospheric variations becomes increasingly important. GNSS users of single frequency receivers and satellite navigation systems need accurate corrections to remove signal degradation effects caused by the ionosphere. Ionospheric modeling using signal processing techniques is the subject of discussion in the present contribution. The next problem under discussion is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness. Reliable and fast knowledge about residential energy consumption at appliance level becomes increasingly important nowadays and it is an important mitigation measure to prevent energy wastage. Energy disaggregation or Nonintrusive load monitoring (NILM) is a single channel blind source separation problem where the task is to estimate the consumption of each electrical appliance given the total energy consumption. For both problems various deep learning models (DL) are proposed that cover various aspects of the problem under study, whereas experimental results indicate the proposed methods superiority compared to the current state of the art.