Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey

Akcan, Serap; Kuvvetli, Yusuf; Koçyiğit, Hasan

doi:10.1080/10807039.2017.1392233

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…This was followed by further prescribed advances in statistics, see [8,9,10,11,12]. The structural time-series model was formilized and described as the classical way of decomposition by [12,13], According to [14], and [15] owing to the increasing global warming in the world, analyzing greenhouse gas emissions is an essential concern. To estimate future emissions, the following time series analysis models: moving average method, exponential smoothing method, and exponential smoothing with trend method were used to estimate greenhouse gas emissions.…”

Section: Methodsmentioning

confidence: 99%

Penalized Maximum Likelihood Estimation of Semiparametric Generalized Linear Models with Application to Climate Temperature Data

Karim¹,

Kube²,

Saeed³

2020

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Global temperature change is an important indicator of climate change. Climate time series data are characterized by trend, seasonal/cyclical as well as irregular components. Adequately modeling these components cannot be overemphasized. In this paper, we have proposed an approach of modeling temperature data using semiparametric additive generalized linear model. We have derived a penalized maximum likelihood estimation of the additive component of the semiparametric generalized linear models, that is, of regression coefficients and smooth functions. A statistical modeling with real time series data set was conducted on temperature data. The study has provided indications on the gain of using semiparametric modeling in situations where a signal component can be additively decomposed in to trend, cyclical and irregular components. Thus, we recommend semiparametric additive penalized models as an option to fit time series data sets in modelling the different component with different functions to adequately explain the relation inherent in data.

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Section: Methodsmentioning

confidence: 99%

Penalized Maximum Likelihood Estimation of Semiparametric Generalized Linear Models with Application to Climate Temperature Data

Karim¹,

Kube²,

Saeed³

2020

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“…[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. [37] predicted GHGs during the period at LTO (landing /take off) of aircrafts at Kahramanmaraş Airport in Turkey.…”

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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“…In recent years, there has been growing interest in applying time series deep learning models for climate predictions in various domains, including indoor hydroponic greenhouses [ 7 , 8 ]. These models can potentially capture the complex relationships between different variables and make accurate predictions based on historical data.…”

Section: Related Workmentioning

confidence: 99%

“…The sigmoid layer of the output gate determines the cell state (according to Equation ( 7)). The result is then passed through the 𝑡𝑡𝑡𝑡𝑡𝑡ℎ function and multiplied by the output of the 𝑠𝑠𝑖𝑖𝑠𝑠𝑠𝑠𝑓𝑓𝑖𝑖𝑠𝑠 gate, which is obtained using Equation (8). C) The 1D-CNN model is a convolutional neural network that takes the input features and passes them through convolutional layers.…”

Section: Models For Forecasting Environmental Changesmentioning

confidence: 99%

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Performance Analysis of Time Series Deep Learning Models for Climate Prediction in Indoor Hydroponic Greenhouses at Different Time Intervals

Eraliev

Lee

2023

Plants

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Indoor hydroponic greenhouses are becoming increasingly popular for sustainable food production. On the other hand, precise control of the climate conditions inside these greenhouses is crucial for the success of the crops. Time series deep learning models are adequate for climate predictions in indoor hydroponic greenhouses, but a comparative analysis of these models at different time intervals is needed. This study evaluated the performance of three commonly used deep learning models for climate prediction in an indoor hydroponic greenhouse: Deep Neural Network, Long–Short Term Memory (LSTM), and 1D Convolutional Neural Network. The performance of these models was compared at four time intervals (1, 5, 10, and 15 min) using a dataset collected over a week at one-minute intervals. The experimental results showed that all three models perform well in predicting the temperature, humidity, and CO2 concentration in a greenhouse. The performance of the models varied at different time intervals, with the LSTM model outperforming the other models at shorter time intervals. Increasing the time interval from 1 to 15 min adversely affected the performance of the models. This study provides insights into the effectiveness of time series deep learning models for climate predictions in indoor hydroponic greenhouses. The results highlight the importance of choosing the appropriate time interval for accurate predictions. These findings can guide the design of intelligent control systems for indoor hydroponic greenhouses and contribute to the advancement of sustainable food production.

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Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey

Cited by 8 publications

References 14 publications

Penalized Maximum Likelihood Estimation of Semiparametric Generalized Linear Models with Application to Climate Temperature Data

Penalized Maximum Likelihood Estimation of Semiparametric Generalized Linear Models with Application to Climate Temperature Data

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

Performance Analysis of Time Series Deep Learning Models for Climate Prediction in Indoor Hydroponic Greenhouses at Different Time Intervals

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