Arturs Stepcenko - Academia.edu (original) (raw)

Uploads

Papers by Arturs Stepcenko

Research paper thumbnail of Forecasting System Development for Nonlinear and Nonstationary Time Series of Normalized Difference Vegetation Index

Professor Dr. tech. sc. Vadim Romanuke Polish Naval Academy, Poland DECLARATION OF ACADEMIC INTEG... more Professor Dr. tech. sc. Vadim Romanuke Polish Naval Academy, Poland DECLARATION OF ACADEMIC INTEGRITY I hereby declare that the Doctoral Thesis submitted for the review to Riga Technical University for the promotion to the scientific degree of Doctor of Engineering Sciences is my own. I confirm that this Doctoral Thesis had not been submitted to any other university for the promotion to a scientific degree. Artūrs Stepčenko ……………………………. (signature) Date: ……………………… The Doctoral Thesis has been written in Latvian. It consists of Introduction; 5 chapters; Conclusion; 36 figures; 21 tables; 4 appendices; the total number of pages is 171, including appendices. The Bibliography contains 121 titles.

Research paper thumbnail of Land Cover Classification Based On MODIS Imagery Data Using Artificial Neural Networks

publication.editionName, 2017

Remote sensing has been widely used to obtain land cover information using automated classificati... more Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.

Research paper thumbnail of NDVI Short-Term Forecasting Using Recurrent Neural Networks

publication.editionName, 2015

In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by s... more In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.

Research paper thumbnail of Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions

Time series of earth observation based estimates of vegetation inform about variations in vegetat... more Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.

Research paper thumbnail of Methods of Forecasting Based on Artificial Neural Networks

This article presents an overview of artificial neural network (ANN) applications in forecasting ... more This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neural network (BPNN), Elman recurrent neural network (ERNN), grey relational artificial neural network (GRANN) and hybrid systems – models that fuse artificial neural network with wavelets and autoregressive integrated moving average (ARIMA)

Research paper thumbnail of Ontology-based Image Representation

This article presents an overview of ontology based digital image representation. An ontology is ... more This article presents an overview of ontology based digital image representation. An ontology is a specification of a conceptualization to create a vocabulary for exchanging information, where conceptualization mean a mapping between symbols used in the computer (i.e., the vocabulary) and objects and relations in the real world. In this paper, digital image semantic annotation by ontology and a novel ontological approach that formalizes concepts and relations with respect to image representations for data mining – the Image Representations Ontology (IROn) – are examined

Research paper thumbnail of Normalized Difference Vegetation Index Forecasting Using a Regularized Layer Recurrent Neural Network

In this paper predictions of the normalized difference vegetation index (NDVI) are discussed. Tim... more In this paper predictions of the normalized difference vegetation index (NDVI) are discussed. Time series of Earth observation based estimates of vegetation inform about changes in vegetation. NDVI is an important parameter for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN's) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. A layer recurrent neural network (LRN) is used in this paper to make one-step-ahead prediction of the NDVI time series.

Research paper thumbnail of NDVI Index Forecasting Using a Layer Recurrent Neural Network Coupled with Stepwise Regression and the PCA

Research paper thumbnail of Nelineāru un nestacionāru normalizēta veģetācijas indeksa laika rindu prognozēšanas sistēmas izstrādāšana

Research paper thumbnail of Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Information Technology and Management Science, 2016

In this paper, the NDVI time series forecasting model has been developed based on the use of disc... more In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI) is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series d...

Research paper thumbnail of Forecasting System Development for Nonlinear and Nonstationary Time Series of Normalized Difference Vegetation Index

Professor Dr. tech. sc. Vadim Romanuke Polish Naval Academy, Poland DECLARATION OF ACADEMIC INTEG... more Professor Dr. tech. sc. Vadim Romanuke Polish Naval Academy, Poland DECLARATION OF ACADEMIC INTEGRITY I hereby declare that the Doctoral Thesis submitted for the review to Riga Technical University for the promotion to the scientific degree of Doctor of Engineering Sciences is my own. I confirm that this Doctoral Thesis had not been submitted to any other university for the promotion to a scientific degree. Artūrs Stepčenko ……………………………. (signature) Date: ……………………… The Doctoral Thesis has been written in Latvian. It consists of Introduction; 5 chapters; Conclusion; 36 figures; 21 tables; 4 appendices; the total number of pages is 171, including appendices. The Bibliography contains 121 titles.

Research paper thumbnail of Land Cover Classification Based On MODIS Imagery Data Using Artificial Neural Networks

publication.editionName, 2017

Remote sensing has been widely used to obtain land cover information using automated classificati... more Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.

Research paper thumbnail of NDVI Short-Term Forecasting Using Recurrent Neural Networks

publication.editionName, 2015

In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by s... more In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.

Research paper thumbnail of Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions

Time series of earth observation based estimates of vegetation inform about variations in vegetat... more Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.

Research paper thumbnail of Methods of Forecasting Based on Artificial Neural Networks

This article presents an overview of artificial neural network (ANN) applications in forecasting ... more This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neural network (BPNN), Elman recurrent neural network (ERNN), grey relational artificial neural network (GRANN) and hybrid systems – models that fuse artificial neural network with wavelets and autoregressive integrated moving average (ARIMA)

Research paper thumbnail of Ontology-based Image Representation

This article presents an overview of ontology based digital image representation. An ontology is ... more This article presents an overview of ontology based digital image representation. An ontology is a specification of a conceptualization to create a vocabulary for exchanging information, where conceptualization mean a mapping between symbols used in the computer (i.e., the vocabulary) and objects and relations in the real world. In this paper, digital image semantic annotation by ontology and a novel ontological approach that formalizes concepts and relations with respect to image representations for data mining – the Image Representations Ontology (IROn) – are examined

Research paper thumbnail of Normalized Difference Vegetation Index Forecasting Using a Regularized Layer Recurrent Neural Network

In this paper predictions of the normalized difference vegetation index (NDVI) are discussed. Tim... more In this paper predictions of the normalized difference vegetation index (NDVI) are discussed. Time series of Earth observation based estimates of vegetation inform about changes in vegetation. NDVI is an important parameter for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN's) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. A layer recurrent neural network (LRN) is used in this paper to make one-step-ahead prediction of the NDVI time series.

Research paper thumbnail of NDVI Index Forecasting Using a Layer Recurrent Neural Network Coupled with Stepwise Regression and the PCA

Research paper thumbnail of Nelineāru un nestacionāru normalizēta veģetācijas indeksa laika rindu prognozēšanas sistēmas izstrādāšana

Research paper thumbnail of Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Information Technology and Management Science, 2016

In this paper, the NDVI time series forecasting model has been developed based on the use of disc... more In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI) is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series d...