Three-day forecasting of greenhouse gas CH4 in the atmosphere of the Arctic Belyy Island using discrete wavelet transform and artificial neural networks

Rakhmatova, Anna; Sergeev, Alexander; Shichkin, Andrey; Buevich, Alexander; Baglaeva, Elena

doi:10.1007/s00521-021-05792-3

Cited by 10 publications

(3 citation statements)

References 51 publications

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“…[34] predicted the effect on GHGs emissions of the end-of-life vehicles (ELV). [35] estimated CH4 emissions by combining wavelet transform and artificial neural networks on the Belyy Island, Russia. [36] studied GHGs emissions in Turkey consistent with energy, industrial products, agribusiness, and barren sectors by using time series models as moving average, exponential smoothing, exponential smoothing with trend.…”

Section: Literature Overviewmentioning

confidence: 99%

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

ÖZTÜRK,

EMİR

2024

KONJES

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The way of life, consumption habits, urbanization rate, type of energy production and increasing energy need with growing economies and population progressively promote the GHGs emissions to Earth’s atmosphere. GHGs consisting of CH4, N2O, CO2, H2O and HFCs cause the climate change, disrupting ecological balance, melting glaciers with global warming in the last decades. Therefore, the issues of future prediction and reduction of GHGs emissions became crucial for policy makers of Turkey and other countries under the international protocols and agreements. This article aims to present the prediction and 8-year future forecasting of CH4, N2O and CO2 emissions of Turkey using past annual data between years 1970 and 2018 with grey, autoregressive integrated moving average and double exponential smoothing models. Based on the results, the best prediction performance is reached by DES model followed by ARIMA and GM for all the emissions. MAPEs calculated from the available data and prediction by DES model from 1970 to 2018 are 0.285, 0.355 and 0.408 for CH4, N2O and CO2 in turn. DES future estimations of CH4, N2O and CO2 at 2026 year are determined as 50700 kiloton of CO2 eq., 38100 thousand metric ton of CO2 eq., and 512000 kilotons.

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Section: Literature Overviewmentioning

confidence: 99%

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

ÖZTÜRK,

EMİR

2024

KONJES

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“…The best accuracy was achieved using NARX [ 32 , 33 ]. The CH4 concentration, temperature, humidity, and pressure values were determined at the same location (Belyy Island, Russia) for approximately two months (July and August 2017) in another study [ 34 ]. The data were averaged hourly, and 1175 data points were used.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE

Yammahi¹,

Aung²

2023

Heliyon

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“…Высокую эффективность для решения таких задач показали ИНС [9][10][11]. В настоящее время ИНС широко используются в системах технического зрения, автоматизированного управления технологическими процессами, в робототехнике, в биомедицине [12][13][14][15][16][17][18]. В ГС нейронные сети нашли применение при решении задач качественного анализа состава газовых смесей [9].…”

Section: Introductionunclassified

Two-Module Neural Network Method of Information Processing in Gas Analysis Systems

Bondar,

Brezhneva,

Botikov

et al. 2023

Proceedings of the SWSU

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Purpose of research: reduction of additional errors in measuring gas concentrations in gas analytical systems (GS) caused by the sensitivity of semiconductor sensors to non-target components of gas mixtures, ambient temperature and humidity. To develop and test a two-module neural network method for processing information in a GS, which allows automating the processes of generating training data and searching for the optimal structure of artificial neural networks (ANNs), reducing errors in reproducing the characteristics of sensors by replacing their mathematical models with neural networks.Methods. Theory of artificial neural networks, numerical methods, simulation methods. To evaluate the effectiveness of the proposed solution, the relative error (d), standard deviation (RMS) were calculated, and comparison with analogues was carried out.Results: a two-module neural network method for processing information in a GS has been studied. Numerical modeling was used to carry out experimental studies on the choice of optimal ANN structures, the volume and composition of training data. In the course of experimental studies, the errors of generating training data using ANN (less than 5%) and determining the concentrations of detected gases under conditions of fluctuations in the parameters of the air environment and the composition of the gas mixture (less than 4%) were calculated.Conclusion. A two-module neural network method for information processing is proposed, which is distinguished by the use of two successive modules of multilayer neural networks for generating training data and processing information coming from the GS sensor unit. The use of an auxiliary module makes it possible to compress the initial data, unify and automate the process of their generation, as well as improve the accuracy of reproduction of multiparameter sensor conversion functions, in comparison with alternative methods. Results of experimental studies of the effectiveness of using the information processing method to reduce additional errors in the quantitative determination of the composition of the air environment under conditions of parameter fluctuations are presented.

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Three-day forecasting of greenhouse gas CH4 in the atmosphere of the Arctic Belyy Island using discrete wavelet transform and artificial neural networks

Cited by 10 publications

References 51 publications

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE

Two-Module Neural Network Method of Information Processing in Gas Analysis Systems

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