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RETRACTED ARTICLE: Coastline climate and coastal library cultural information management based on geographic information system (GIS)

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This article was retracted on 23 November 2021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

Typhoon is caused by the action of sea level and atmosphere. It is a kind of natural disaster with relatively strong destructive ability, which will seriously affect the development of coastal areas. Typhoon weather usually causes large-scale rainstorms and strong winds. Strong winds and heavy rains will form storm surges and large waves on the sea, and the damage to the coast is very huge and obvious. In order to reduce the damage caused to people by typhoon weather, researchers need to conduct specific analysis on the occurrence of typhoons. Only by grasping the trajectory and law of typhoon activities can they provide a basis for people to prevent typhoon disasters in advance. In the data analysis, this study built a functional analysis model in the geographic information system, used the pixel scale to measure the specific situation of typhoon landing, and combined the population distribution and total economic growth in my country’s coastal areas. A further analysis was made on the spatial distribution of typhoons, and the results of the analysis can provide certain help to the economic development of coastal areas in my country. This article also studies the development of tourism in coastal areas, puts forward effective suggestions for the establishment and development of library culture, and advocates that people make full use of the advantages of library culture to improve the level of tourism development in coastal areas.

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References

  • Al-Rawas AA (2000) State-of-the-art-review of collapsible soils. Sci Technol Rev 5:115–135

    Google Scholar 

  • Anderson FJ (1968) Collapsing soils and their basic parameters in an area in the Tucson, Arizona vicinity, MSc Thesis, The University of Arizona

  • Ayadat T, Hanna A (2007) Prediction of collapse behaviour in soil. Eur J Environ Civ En 11(5):603–619

    Google Scholar 

  • Benchouk A, Abou-Bekr N, Taibi S (2013) Potential collapse for a clay soil. J Emerging Technol Adv Eng 3(10):43–47

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378

    Article  Google Scholar 

  • Choudhury C, Bharat TV (2015) Collapse behavior of clay soil under one-dimensional (1D) compression condition. 50th Indian Geotechnical Conference, Maharashtra, India (2):18–27

  • Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250

    Article  Google Scholar 

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • Cui K, Lu D, Li W (2017) Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto Int 32:935–955

    Article  Google Scholar 

  • Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 135:122–148

    Article  Google Scholar 

  • Dempster AP (2008) Upper and lower probabilities induced by a multivalued mapping. In: Classic works of the Dempster-Shafer theory of belief functions. Springer, Berlin Heidelberg, pp 57–72

    Chapter  Google Scholar 

  • Esmaeili-choobar N, Esmaeili-falak M, Roohi-hir M, Keshtzad S (2013) Evaluation of collapsibility potential at Talesh, Iran. EJGE:2561–2573

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    Google Scholar 

  • Houston W, Mahmoud H, Houston S (1993) A laboratory procedure for partial-wetting collapse determination. Unsaturated Soils, Special Geotechnical Publication, ASCE 39:54–63

    Google Scholar 

  • Houston SL, Houston WN, Mahmoud HH (1995) Interpretation and comparison of collapse measurement techniques. In: Genesis and properties of collapsible soils. Springer, Dordrecht, pp 217–224

    Chapter  Google Scholar 

  • Houston SL, Houston WN, Zapata CE, Lawrence C (2001) Geotechnical engineering practice for collapsible soils. In: Unsaturated soil concepts and their application in geotechnical practice. Springer, Dordrecht, pp 333–355

    Chapter  Google Scholar 

  • Houston SL, Houston WN, Lawrence CA (2002) Collapsible soil engineering in highway infrastructure development. J Transp Eng-Asce 128(3):295–300

    Article  Google Scholar 

  • Huang F, Yao C, Liu W, Li Y, Liu X (2018) Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine. Geomat Nat Hazards Risk 9:919–938

    Article  Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926

    Article  Google Scholar 

  • Johari A, Nejad AH (2015) Prediction of soil-water characteristic curve using gene expression programming. Iran J Sci Technol A 39(C1):143

    Google Scholar 

  • Juang CH, Elton DJ (1997) Prediction of collapse potential of soil with neural networks. Transp Res Rec 1582:22–28

    Article  Google Scholar 

  • Khalili N, Geiser F, Blight G (2004) Effective stress in unsaturated soils: review with new evidence. Int J Geomech 4(2):115–126

    Article  Google Scholar 

  • Li P, Vanapalli S, Li T (2016) Review of collapse triggering mechanism of collapsible soils due to wetting. J Rock Mech Geotech Eng 8(2):256–274

    Article  Google Scholar 

  • Lim YY, Miller GA (2004) Wetting-induced compression of compacted Oklahoma soils. J Geotech Geoenviron 130(10):1014–1023

    Article  Google Scholar 

  • Nazari A (2012) Prediction performance of PEM fuel cells by gene expression programming. Int J Hydrog Energy 37(24):18972–18980

    Article  Google Scholar 

  • Rabbi ATMZ, Cameron DA, Rahman MM (2014) Role of matric suction on wetting-induced collapse settlement of silty sand. Unsaturated Soils: Research & Applications (5):129–135

  • Sarıdemir M (2011) Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming. Expert Syst Appl 38(11):14257–14268

    Google Scholar 

  • Sarıdemir M (2014) Effect of specimen size and shape on compressive strength of concrete containing fly ash: application of genetic programming for design. Mater Des 56:297–304

    Article  Google Scholar 

  • Sarıdemir M, Severcan MH (2016) The use of genetic programming and regression analysis for modeling the modulus of elasticity of NSC and HSC. Arab J Sci Eng 41(10):3959–3967

    Article  Google Scholar 

  • Schanz T, Karim HH (2018) Geotechnical characteristics of some Iraqi gypseous soils. In: MATEC Web of Conferences, vol 162. EDP Sciences (3):01005

  • Taskiran T (2010) Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv Eng Softw 41:886–892

    Article  Google Scholar 

  • Tenpe A, Patel A (2018) Application of genetic expression programming and artificial neural network for prediction of CBR. Road Mater Pavement 9(1):1–18

    Google Scholar 

Download references

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Correspondence to Mingyin Wu.

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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/s12517-021-09078-9

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Wu, M. RETRACTED ARTICLE: Coastline climate and coastal library cultural information management based on geographic information system (GIS). Arab J Geosci 14, 940 (2021). https://doi.org/10.1007/s12517-021-07211-2

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  • DOI: https://doi.org/10.1007/s12517-021-07211-2

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