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RETRACTED ARTICLE: Green city economic efficiency based on cloud computing and machine learning

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This article was retracted on 23 November 2021

An Editorial Expression of Concern to this article was published on 28 September 2021

This article has been updated

Abstract

In the early development process, my country destroyed the development of the natural environment and the allocation of natural resources in pursuit of the speed of economic development. With the acceleration of development, my country’s economic construction and social development have achieved good results. To achieve sustainable economic and ecological development in the future, green and high-quality development must be achieved to meet the content of the development strategy of realizing the resource-based and economical society in our country. In the long-term development process, many regions rely on resources to create a lot of opportunities for the development of the region, but with the high consumption of resources, many resources are non-renewable or the regeneration process is very slow, resulting in many regions entering the resources. During the period of shortage, it is necessary to advocate green development. This article analyzes the green economic benefits achieved by the innovative development of a certain province in my country by studying the relevant knowledge of machine learning and some important issues in the theoretical development of cloud computing, which can provide a certain reference for the development of economic efficiency theory and provide help for people to formulate green development strategies for cities and regions.

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References

  • Alam SK, Mondal A, Shiuly A (2020) Prediction of CBR value of fine grained soils of Bengal Basin by genetic expression programming, artificial neural network and krigging method. J Geol Soc India 95(2):190–196

    Article  Google Scholar 

  • Alkroosh I, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25(3):618–627

    Article  Google Scholar 

  • Almahbobi S (2018) Experimental study of volume change and shear strength behaviour of statically compacted collapsible soil. Ph.D. Dissertation, Cardiff University

  • Barden L, McGown A, Collins K (1973) The collapse mechanism in partly saturated soil. Eng Geol 7(1):49–60

    Article  Google Scholar 

  • Basma AA, Tuncer ER (1992) Evaluation and control of collapsible soils. J Geotech Eng 118(10):1491–1504

    Article  Google Scholar 

  • Cheng ZL, Zhou WH, Garg A (2020) Genetic programming model for estimating soil suction in shallow soil layers in the vicinity of a tree. Eng Geol 105506

  • El Howayek A, Huang PT, Bisnett R, Santagata MC (2011) Identification and behavior of collapsible soils. FHWA/IN/JTRP-2011/12, Joint Transportation Research Program 45–50

  • Ferreira C (2003) Function finding and the creation of numerical constants in gene expression programming. In: Benitez JM, Cordon O, Hoffmann F, Roy R (eds) Advances in soft computing: engineering design and manufacturing. Springer-Verlag, pp 257–266

  • Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence, vol 21. Springer

  • Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput & Applic 21(1):189–201

    Article  Google Scholar 

  • Habibagahi G, Taherian M (2004) Prediction of collapse potential for compacted soils using artificial neural networks. Sci Iran 11(1&2):1–20

    Google Scholar 

  • Hasanzadehshooiili H, Mahinroosta R, Lakirouhani A, Oshtaghi V (2014) Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels. Arab J Geosci 7(6):2303–2314

    Article  Google Scholar 

  • Hodek RJ, Lovell CW (1979) A new look at compaction process in fills. Bull Assoc Eng Geol 16(4):487–499

    Google Scholar 

  • Houston SL, Houston WN (1997) Collapsible soils engineering. In: Houston SL (ed) Unsaturated soil engineering practice. ASCE, pp 199–232

  • Houston SL, Houston WN, Spadola DJ (1988) Prediction of field collapse of soils due to wetting. J Geotech Engrs Div ASCE 114(1):40–58

    Article  Google Scholar 

  • İnce İ, Bozdağ A, Fener M, Kahraman S (2019) Estimation of uniaxial compressive strength of pyroclastic rocks (Cappadocia, Turkey) by gene expression programming. Arab J Geosci 12(24):756

    Article  Google Scholar 

  • Jahed Armaghani D, Faradonbeh RS, Momeni E, Fahimifar A, Tahir MM (2018) Performance prediction of tunnel boring machine through developing a gene expression programming equation. Eng Comput 34(1):129–141

    Article  Google Scholar 

  • Javadi AA, Rezania M (2009) Intelligent finite element method: an evolutionary approach to constitutive modelling. Adv Eng Inform 23:442–451

    Article  Google Scholar 

  • Khuntia S (2014) Modelling of geotechnical problems using soft computing. Ph.D. Dissertation, National Institute of Technology, Rourkela

  • Knodel PC (1992) Characteristics and problems of collapsible soils. Rep./US. Bureau of reclamation N R-92-02

  • Lawton EC, Fragaszy RJ, James HH (1989) Collapse of compacted clayey sand. J Geotech Eng ASCE 155(9):1252–1267

    Article  Google Scholar 

  • Lawton EC, Fragaszy RJ, Hetherington MD (1992) Review of wetting-induced collapse in compacted soil. J Geotech Eng 118(9):1376–1394

    Article  Google Scholar 

  • Leong EC, Widiastuti S, Rahardjo H (2013) Estimating wetting-induced settlement of compacted soils using oedometer test. Geotech Eng 44(1):26–33

    Google Scholar 

  • Mahdiyar A, Jahed Armaghani D, Koopialipoor M, Hedayat A, Abdullah A, Yahya K (2020) Practical risk assessment of ground vibrations resulting from blasting, using gene expression programming and Monte Carlo simulation techniques. Appl Sci 10(2):472

    Article  Google Scholar 

  • Najemalden AM, Ibrahim SW, Ahmed MD (2020) Prediction of collapse potential for gypseous sandy soil using ANN technique. J Eng Sci Technol 15(2):1236–1253

    Google Scholar 

  • Okonta FN (2012) Collapse settlement behavior of remoulded and undisturbed weathered quartzite. Int J Phys Sci 7(32):5239–5247

    Google Scholar 

  • Pereira JH, Fredlund DG (2000) Volume change behavior of collapsible compacted Gneiss soil. J Geotech Geoenviron 126(10):859–946

    Article  Google Scholar 

  • Rabbi ATMZ, Cameron DA (2014) Prediction of collapse potential for silty glacial. Aust Geomech 49:65–78

    Google Scholar 

  • Rabbi ATMZ, Cameron DA, Rahman MM (2014) Effect of initial partial saturation on collapse behavior of glacial sand with fines. In: Geo-Congress 2014: geo-characterization and modeling for sustainability, pp 103–112

  • Shahin MA (2013) Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. Metaheuristics in water, geotechnical and transport engineering, pp 16-204, Elsevier

  • Shalaby SI (2017) Potential collapse for sandy compacted soil during inundation. Int J Innov Sci Eng Technol 4(5):307–314

    Google Scholar 

  • Tadepalli R, Fredlund DG (1991) The collapse behavior of a compacted soil during inundation. Can Geotech J 28(4):477–488

    Article  Google Scholar 

  • Wang LJ, Guo M, Sawada K, Lin J, Zhang J (2015) Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena 135:271–282

    Article  Google Scholar 

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Correspondence to Zhang Jin.

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Responsible Editor: Sheldon Williamson

This article is part of the Topical Collection on Environment and Low Carbon Transportation

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-021-09070-3

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Jin, Z. RETRACTED ARTICLE: Green city economic efficiency based on cloud computing and machine learning. Arab J Geosci 14, 1007 (2021). https://doi.org/10.1007/s12517-021-07204-1

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  • DOI: https://doi.org/10.1007/s12517-021-07204-1

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