Abstract
The main research content of this paper is VR visualization allocation algorithm which can realize memory optimization. The use of this algorithm can effectively reduce the redundancy of modeling and centralized processing of multiple models, reduce the time consumption, improve the efficiency of modeling, and ensure the smooth operation of VR visualization system. It is important to note that the economic development of the region will affect the visualization effect, especially when studying the water shortage in high-level areas, so in the actual water resource problem-solving process, the cross basin water transfer method is often adopted. The inter basin water transfer project needs to span the distance of time and space in the process of implementation. It has the characteristics of multi-objective, multi task, dynamic change, and unknown. These characteristics determine that the inter basin water transfer project is a complex and arduous task. Water diversion project is born to solve the uneven distribution of regional water resources, which can effectively solve the problem of water supply and consumption. In addition, the water diversion project can also improve the ecological conditions of the region and can also improve the quality of drinking water in the receiving area, so as to improve the quality of life of the people in the region. With the continuous progress of society, urban construction began to pay more attention to public art. This paper studies the current situation of public art in modern cities in China and puts forward plans and suggestions in line with China’s national conditions according to the actual situation and prospects the future development of urban art in China. Urban public art originated in Western countries and developed rapidly after entering China. It is perfectly integrated with Chinese culture, forming a unique development path for China.
Similar content being viewed by others
Change history
18 November 2021
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12517-021-08993-1
28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08471-8
References
Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10:194. https://doi.org/10.1007/s12517-017-2980-6
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Afungang RN, de Meneses Bateira CV, Nkwemoh CA (2017) Assessing the spatial probability of landslides using GIS and informative value model in the Bamenda highlands. Arab J Geosci 10:384. https://doi.org/10.1007/s12517-017-3155-1
Bahrami S, Rahimzadeh B, Khaleghi S (2019) Analyzing the effects of tectonic and lithology on the occurrence of landslide along Zagros ophiolitic suture: a case study of Sarv-Abad, Kurdistan. Iran Bull Eng Geol Environ 79:1–19. https://doi.org/10.1007/s10064-019-01639-3
Banshtu RS, Versain LD, Pandey DD (2020) Risk assessment using quantitative approach: central Himalaya, Kullu, Himachal Pradesh, India. Arab J Geosci 13:1–11. https://doi.org/10.1007/s12517-020-5143-0
Bui DT, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444. https://doi.org/10.1007/s11069-011-9844-2
Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423. https://doi.org/10.1007/s10346-010-0202-3
Chen T, Niu R, Jia X (2016) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75:867. https://doi.org/10.1007/s12665-016-5317-y
Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena. 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Corominas J, Van Westen CJ, Frattini P et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
Dikshit A, Sarkar R, Pradhan B, Acharya S, Alamri AM (2020) Spatial landslide risk assessment at Phuentsholing, Bhutan. Geosciences 10:131. https://doi.org/10.3390/geosciences10040131
Dunning SA, Mitchell WA, Rosser NJ, Petley DN (2007) The Hattian Bala rock avalanche and associated landslides triggered by the Kashmir earthquake of 8 October 2005. Eng Geol 93:130–144. https://doi.org/10.1016/j.enggeo.2007.07.003
Francioni M, Calamita F, Coggan J et al (2019) A multi-disciplinary approach to the study of large rock avalanches combining remote sensing, GIS and field surveys: the case of the Scanno landslide, Italy. Remote Sens 11:1570. https://doi.org/10.3390/rs11131570
Freeman P, Martin L, Mechler R, Warner K (2004) A methodology for incorporating natural catastrophes into macroeconomic projections. Disaster Prev Manag An Int J 13:337–342. https://doi.org/10.1108/09653560410556564
Gill JC, Malamud BD (2017) Anthropogenic processes, natural hazards, and interactions in a multi-hazard framework. Earth-Sci Rev 166:246–269. https://doi.org/10.1016/j.earscirev.2017.01.002
Guri PK, Patel RC (2015) Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling. Environ Monit Assess 187:324. https://doi.org/10.1007/s10661-015-4535-1
Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City, china. Arab J Geosci 9:112. https://doi.org/10.1007/s12517-015-2094-y
Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology. 101:631–642. https://doi.org/10.1016/j.geomorph.2008.03.003
Kaur H, Gupta S, Parkash S, Thapa R, Gupta A, Khanal GC (2019) Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques. Ann GIS 25:113–132. https://doi.org/10.1080/19475683.2019.1575906
Mahdadi F, Boumezbeur A, Hadji R, Kanungo DP, Zahri F (2018) GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, NE Algeria. Arab J Geosci 11:476. https://doi.org/10.1007/s12517-018-3770-5
Mahmood I, Qureshi SN, Tariq S, Atique L, Iqbal MF (2015) Analysis of landslides triggered by October 2005, Kashmir Earthquake. PLoS Curr 7. https://doi.org/10.1371/currents.dis.0bc3ebc5b8adf5c7fe9fd3d702d44a99
Martha TR, Van Westen CJ, Kerle N et al (2013) Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology 184:139–150. https://doi.org/10.1016/j.geomorph.2012.12.001
Pradhan AMS, Dawadi A, Kim YT (2012) Use of different bivariate statistical landslide susceptibility methods: a case study of Khulekhani watershed, Nepal. J Nepal Geol Soc 44:1–12
Rai PK, Mohan K, Kumra VK (2014) Landslide hazard and its mapping using remote sensing and GIS. J Sci Res 58:1–13
Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the Sera River basin using logistic regression model. Nat Hazards 85:1323–1346. https://doi.org/10.1007/s11069-016-2591-7
Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenviron Disasters 3:19. https://doi.org/10.1186/s40677-016-0053-x
Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69. https://doi.org/10.1007/s10346-004-0039-8
Acknowledgments
2021 Shaanxi Provincial Philosophy and Social Science Major Theoretical and Practical Issues Research Project “Scientific Evaluation and Innovative Utilization of Modern Architectural Heritage in the Upper and Middle of Han River Basin” (Project No.2021ND0037); 2021 Shaanxi Provincial Social Science Foundation Annual Project “Research on Inheritance of Intangible Cultural Heritage and Rural Revitalization in Qinba Mountain Area.”
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Additional information
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/s12517-021-08993-1
About this article
Cite this article
Huang, W., Lin, H. & Zhang, N. RETRACTED ARTICLE: Cross basin water diversion project based on VR visual system and urban public art design. Arab J Geosci 14, 1733 (2021). https://doi.org/10.1007/s12517-021-08029-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-08029-8