Investigating the trend of average changes of annual temperatures in Iran

Mahmoudi, Peyman; Mohammadi, Majid; Daneshmand, Hojatollah

doi:10.1007/s13762-018-1664-4

Cited by 15 publications

(5 citation statements)

References 21 publications

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“…Due to its extensive effects across all industries globally, climate change is one of the greatest issues confronting humanity [1]. The global average yearly temperature is rising significantly, based on the latest studies [2]. Human activities are substantially Discussion on this paper must be submitted within two months of the print publication.…”

Section: Introductionmentioning

confidence: 99%

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Jian-qing

Wu²,

Zhang

et al. 2022

Structural Concrete

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As one of the significant generators of greenhouse gases, the construction sector is under tremendous pressure due to the rising concern about global climate change and its detrimental effects on communities. Due to the environmental problems connected to the manufacture of cement, geopolymer concrete () has become a viable option for building materials. The results of this research assist in creating machine learning approaches for predicting the qualities of eco‐friendly concrete, which may be used as an alternative to conventional concrete to help lower emissions in the construction sector. In the current work, three hybrid techniques are used to create predictive models that estimate the compressive strength of when ground granulated blast‐furnace slag () is replaced with natural zeolite (), silica fume (), and various concentrations of . For this purpose, three machine learning methods were considered, named , , and hybridized with Fire Hawk Optimization (). The findings show that all approaches have excellent performance in projecting the of , both in terms of the permitted correlation between observed and predicted values. In conclusion, according to the results provided considering statistical indices, error distribution, and Taylor diagram analysis, it could be resulted that the proposed could be recognized as the outperformed model, where other algorithms were also trustable in prediction procedure of of .

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Section: Introductionmentioning

confidence: 99%

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Jian-qing

Wu²,

Zhang

et al. 2022

Structural Concrete

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“…However, no study was found in the literature related to the assessment of the trends in the bioclimate of Iran, and therefore, it was not possible to compare the findings of the present study. However, the trend in temperatures in Iran was assessed in a number of previous studies [47,[85][86][87][88][89][90].…”

Section: Discussionmentioning

confidence: 99%

Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

et al. 2019

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Changes in bioclimatic indicators can provide valuable information on how global warming induced climate change can affect humans, ecology and the environment. Trends in thermal bioclimatic indicators over the diverse climate of Iran were assessed in this study to comprehend their spatio-temporal changes in different climates. The gridded temperature data of Princeton Global Meteorological Forcing with a spatial resolution of 0.25° and temporal extent of 1948–2010 was used for this purpose. Autocorrelation and wavelets analyses were conducted to assess the presence of self-similarity and cycles in the data series. The modified version of the Mann–Kendall (MMK) test was employed to estimate unidirectional trends in 11 thermal bioclimatic indicators through removing the influence of natural cycles on trend significance. A large decrease in the number of grid points showing significant trends was noticed for the MMK in respect to the classical Mann–Kendall (MK) test which indicates that the natural variability of the climate should be taken into consideration in bioclimatic trend analyses in Iran. The unidirectional trends obtained using the MMK test revealed changes in almost all of the bioclimatic indicators in different parts of Iran, which indicates rising temperature have significantly affected the bioclimate of the country. The semi-dry region along the Persian Gulf in the south and mountainous region in the northeast were found to be more affected in terms of the changes in a number of bioclimatic indicators.

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“…Climate change is one of the biggest challenges facing humankind (Shahmansouri et al , 2021) and as a direct consequence of construction activities, is happening faster than previously predicted (Yazdani et al , 2020b). According to recent research studies, the world’s climate is experiencing a significant increase in average annual temperatures (Mahmoudi et al , 2019a). Meanwhile, the atmospheric concentrations of some greenhouse gases (GHGs), particularly carbon dioxide (CO 2 ), engendered by construction activities, exacerbate climate change (Kabirifar et al , 2020; Yazdani et al , 2020a).…”

Section: Introductionmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

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Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

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Investigating the trend of average changes of annual temperatures in Iran

Cited by 15 publications

References 21 publications

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

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