A two-stage methodology based on ensemble Adaptive Neuro-Fuzzy Inference System to predict carbon dioxide emissions

Mardani, Abbas; Fan, Yee Van; Nilashi, Mehrbakhsh; Hooker, Robert E.; Ozkul, Seckin; Štreimikienė, Dalia; Loganathan, Nanthakumar

doi:10.1016/j.jclepro.2019.05.153

Cited by 34 publications

(12 citation statements)

References 51 publications

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“…In the second phase, it uses the test and confirming groups for the justification and simplification of the framework, and the third phase is testing. Mardani et al [22] presented the ANFIS framework for establishing a prediction framework that depends on genuine information. There are five principal layers for creating the forecasting of the framework.…”

Section: Methodsmentioning

confidence: 99%

“…The review of recent literature has emphasized the utilization of advanced methodology to strengthen the validity of derived empirical findings. Hence, in order to predict the energy intensity in the top five ASEAN countries, the present study employed the ANFIS method, which is widely used for complex model problems [22,37,38]. Primarily, the utilization of a mere fuzzy logic system is criticized for having nonappearance of the amenities, which need to be attained from the sample information.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The combination of these two methods is called neuro-fuzzy. Given the importance of environmental quality in current economies, Mardani et al [22] utilized an ANFIS approach for predicting the impact of CO 2 emissions in G8+5 economies between 1990 and 2016. Moreover, Mardani et al [38] used the same method for analysing the link between energy usage, the environment, and growth in G20 nations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In response, the current research has adopted the novel method of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to examine the predictive capacity of financial advancements and globalization in influencing energy intensity. The significance of ANFIS lies in investigating the associations among the variables that are unexplored by traditional methodologies by amalgamating the benefits of fuzzy inference systems and artificial neural networks together to gain superior insights into the dependence structure [22]. Moreover, ANFIS is also vital to resolve the econometric limitations in non-linear associations, thereby being considered efficient for analysing non-linear behaviour [23].…”

Section: Introductionmentioning

confidence: 99%

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An Investigation of an Adaptive Neuro-Fuzzy Inference System to Predict the Relationship among Energy Intensity, Globalization, and Financial Development in Major ASEAN Economies

et al. 2020

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The enhancement of the financial sector significantly drives a nation’s economy and thereby increase energy intensity. Considering this situation, the current study aims to examine the link between globalization and financial advancements with the energy intensity of the top 5 ASEAN (Association of Southeast Asian Nations) economies. The development structure of the ASEAN region is considered significant for having stable growth. The authors used the annual data from 1990 to 2018 for five of the largest ASEAN economies: Singapore, Malaysia, Thailand, Indonesia, and the Philippines. The present study used novel methodology, the Adaptive Neuro-Fuzzy Inference System (ANFIS), to examine the nonlinear behaviour among globalization, financial development, and energy intensity in the top 5 ASEAN countries. The study results using ANFIS confirm that globalization and financial development are positively correlated and have a significant impact on the energy intensity level in the top ASEAN countries. The results further suggest that globalization and financial development increase the level of energy intensity more in the countries that are developed relative to their peers in the top ASEAN countries. Moreover, the outcomes of ANFIS also suggest that those countries, which are more globalized and financially developed, have more potential to increase the level of energy intensity. Therefore, the government needs to focus more on projects that involve renewable energy and are environmentally friendly.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Investigation of an Adaptive Neuro-Fuzzy Inference System to Predict the Relationship among Energy Intensity, Globalization, and Financial Development in Major ASEAN Economies

et al. 2020

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“…Hao et al (2018) put forward a peak prediction model for corporate carbon emission based on gray neural network models to foretell peak corporate carbon emissions, so as to help companies understand their carbon emissions and design carbon emissions reduction paths [32]. Mardani et al (2019) find that the BPNN model could be used well to predict China's carbon emissions by following the principle of Grey Relational Analysis (GRA). A holistic, adaptive neuro-fuzzy inference system is proposed to learn to predict and analyze the relationship between renewable energy consumption, economic growth, and carbon dioxide, while forecasting carbon emissions [33].…”

Section: Carbon Emission Prediction By Neural Networkmentioning

confidence: 99%

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Jin

2021

PLoS ONE

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Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

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Alternative Scenarios in Analyzing Florida’s Freight Imbalance

Ozkul

Davis

Pinjari

et al. 2019

Lecture Notes in Computer Science

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A two-stage methodology based on ensemble Adaptive Neuro-Fuzzy Inference System to predict carbon dioxide emissions

Cited by 34 publications

References 51 publications

An Investigation of an Adaptive Neuro-Fuzzy Inference System to Predict the Relationship among Energy Intensity, Globalization, and Financial Development in Major ASEAN Economies

An Investigation of an Adaptive Neuro-Fuzzy Inference System to Predict the Relationship among Energy Intensity, Globalization, and Financial Development in Major ASEAN Economies

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Alternative Scenarios in Analyzing Florida’s Freight Imbalance

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