Modeling and Forecasting of Carbon Dioxide Emissions in Bangladesh Using Autoregressive Integrated Moving Average (ARIMA) Models

Rahman, Abdur; Hasan, Mahmudul

doi:10.4236/ojs.2017.74038

Cited by 45 publications

(24 citation statements)

References 7 publications

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“…A seasonal ARIMA model is denoted by ARIMA (p, d, q), where p is the number of autoregressive terms, q is the number of moving average terms, and d represents the number of differences applied to the series [52].…”

Section: Arima Modelmentioning

confidence: 99%

Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

et al. 2021

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Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 0–20 cm and p < 0.005 at 0–50 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (2010–2019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.

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Section: Arima Modelmentioning

confidence: 99%

Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

et al. 2021

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“…With reference to the used predictive models for GHG, a wide variety was found in literature, such as Grey model (A. Ö. Dengiz, Atalay, and O. Dengiz, 2018), autoregressive integrated moving average (ARIMA) (Rahman and Hasan, 2017), artificial neural networks (ANNs) (Abdullah and Pauzi, 2015), Long Short-Term Memory (LSTM) network (Ameyaw, L. Yao, Y. Huang, Shen, and H. Liu, 2019;Ameyaw and L. Yao, 2018), and clustering (Poucin, Farooq, and Patterson, 2018).…”

Section: Literaturementioning

confidence: 99%

Advancements in multi-objective eco-routing solutions using connectivity

Alfaseeh¹

2021

Preprint

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Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As potential solutions to mitigate GHG emissions, several approaches have been proposed to better control traffic and manage transportation systems. The employment of Intelligent Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose of routing, this research aims to exploit the advancements in ICT by including connected and automated vehicles (CAVs) and sensing technology in an urban congested network.<div>Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network. The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs was examined for various traffic conditions. E2ECAV was adopted for both the myopic and anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing application, predictive models were developed based on Long-Short Term Memory (LSTM), a deep learning approach, while considering a high level of spatial (link level) and temporal (one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters. The anticipatory routing strategy significantly outperformed the myopic routing strategy based on the the traffic and environmental perspectives. This research shows that ITS can help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an urban network. Furthermore, the proposed anticipatory routing framework can be applied in a real-time situation. </div>

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“…Naturally, there should be a balance between the CO 2 emitted from animals and other sources and the CO 2 utilized by plants during photosynthesis, this balance has been distorted by human activities. Reference [2] pointed out that this imbalance is due to greenhouse effect (global warming, melting of polar ice sheet, rise in sea level and coastal inundation, and damage to agriculture and natural ecosystem). Many human activities have also resulted in an increasing emission of global greenhouse gas (GHG), largely by burning fossil fuels to generate electricity, heat and cool buildings, and power vehicles-as well as by clearing forests [3].…”

Section: Introductionmentioning

confidence: 99%

“…The author used four input variables namely global oil, natural gas, coal and primary energy consumption to predict CO 2 emission. Reference [2] adopted the autoregressive integrated moving average (ARIMA) models to forecast yearly CO 2 prediction in Bangladesh. Different parametric models of ARIMA were constructed and different metrics were adopted to evaluate each ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction

2021

IJATCSE

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In this study, a Takagi-Sugeno-Kang based intuitionistic fuzzy logic system is proposed for the prediction of global carbon dioxide emission for the first time. The intuitionistic fuzzy logic system is an integration of artificial neural network learning and intuitionistic fuzzy logic reasoning. The gradient descent back propagation is applied in the optimization of the parameters of the proposed model. The model is evaluated based on some performance metrics. Results of evaluation revealed that the intuitionistic fuzzy logic system outperforms other existing models in the literature in terms of prediction accuracy

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Modeling and Forecasting of Carbon Dioxide Emissions in Bangladesh Using Autoregressive Integrated Moving Average (ARIMA) Models

Cited by 45 publications

References 7 publications

Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

Advancements in multi-objective eco-routing solutions using connectivity

Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction

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