Abstract
In this paper, the loess slope formed in S province is taken as an example to illustrate the ecological restoration project of limestone quarry in T city. Consistent with the study of groundwater characteristics of high fill loess, based on the incomplete characteristics of loess and the anti-sliding property of high fill loess slope, the water quality curves of samples with different particle sizes and the data of soil characteristic curves obtained through experiments were measured by GCT (groundwater curve tool) using finite element method under different rainfall infiltration conditions. The rainfall erosion is analyzed by MATLAB software, and the unsaturation of loess is analyzed by the data of particle size distribution. Study the influence of groundwater properties; the mathematical model of rainfall erosion behavior curve (SWCC) suitable for the samples studied in this paper is determined, and the influence of various changes in the parameters of SWCC is studied. Finally, based on this point, the paper puts forward the measures to enhance the level of enterprise financial control in the new era, analyzes the problems in preventing financial risks and business control in the era of big data, and analyzes the enterprise financial control system. After that, in order to reasonably use big data technology to maintain and control financial risk, the paper puts forward some suggestions, such as strengthening the professional skills of all employees, improving their comprehensive quality, and enhancing their awareness of early warning financial risk. Based on the above three parts of innovation and reform optimization suggestions, this paper studies the rainfall characteristics of loess slope and the control of enterprise financial risk according to the wide enterprise internal financial control data and successfully puts forward the development measures to improve the enterprise internal financial control in the new period.
Similar content being viewed by others
Change history
15 November 2021
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12517-021-08944-w
28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08471-8
References
Abdelmadjid B, Omar S (2013) Assessment of groundwater pollution by nitrates using intrinsic vulnerability methods: a case study of the Nil valley groundwater (Jijel, North-East Ageria). Afri J Environ Sci Tech 7(10):949–960
Ahmed I, Nazzal Y, Zaidi F (2018) Groundwater pollution risk mapping using modified DRASTIC model in parts of Hail region of Saudi Arabia. Environ Eng Res 23(1):84–91. https://doi.org/10.4491/eer.2017.072
Almasri MN (2007) Nitrate contamination of groundwater: a conceptual management framework. Environ Impact Assess Rev 27(3):220–242
Azubuike SE, Edet AE (2015) Vulnerability assessment of aquifers within the Oban Massif, South-Eastern Nigeria, using DRASTIC Method. Int J Sci Eng Res 6:1123–1135
Babiker IS, Mohamed MAA, Terao H, Kato K, Ohta K (2004) Assessment of groundwater contamination by nitrate leaching from intensive vegetable cultivation using geographical information system. Environ Int 29(8):1009–1017
Bazimenyera D, Zhnoghua T (2008) A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Hangzhou-Jiaxing-Huzhou Plain. China Med Well Res J Appl Sci 8(3):550–559
Buczko U, Kuchenbuch RO, Lennartz B (2010) Assessment of the predictive quality of simple indicator approaches for nitrate leaching from agricultural fields. J Environ Manag 91:1305–1315
Chroeder JJ, Scholefield D, Cabral F, Hofman G (2004) The effect of nutrient losses from agriculture on ground and surface water quality: the position of science in developing indicators for regulation. Environ Sci Pol 7:15–23
Civita M, De Miao M (2004) Assessing and mapping groundwater vulnerability to contamination: the Italian combined approach. Geofis Int 43:513–532
Corniello A, Ducci D, Monti GM (2004) Aquifer pollution vulnerability in the Sorrento peninsula, southern Italy, evaluated by SINTACS method. Geofis Int 43(4):575–581
Elisante E, Muzuka ANN (2016) Assessment of sources and transformation of nitrate in groundwater on the slopes of Mount Meru, Tanzania. Environ Earth Sci 75(3):1–15
Ezeh CC (2011) Geoelectrical studies for estimating aquifer hydraulic properties in Enugu state, Nigeria. Int J the Physical Sci 6(14):3319–3329
Fazelabdolabadi B, Golestan MH (2020) Towards Bayesian quantification of permeability in micro-scale porous structures—the database of micro networks. Hi-Tech Innova J 1(4):148–160. https://doi.org/10.28991/HIJ-2020-01-04-02
Fırat EA, Ersoy H, Gültekin F (2006) Nitrate, nitrite and ammonia contamination in groundwater: a case study from Gümüşhacıköy Plain, Turkey. Asian J Wat Environ Pollut 4(1):107–118
Goldscheider N, Klute M, Sturm S (2000) The PI method—a GIS-based approach to mapping groundwater vulnerability with special consideration of karst aquifers. Z Angew Geol 46(3):157–166
Houria B, Mahdi K, Zohra TF (2020) Hydrochemical characterisation of groundwater quality: Merdja Plain (Tebessa Town, Algeria). Civil Eng J 6(2):318–324. https://doi.org/10.28991/cej-2020-03091473
Hussain MR, Abed BS (2019) Simulation and assessment of groundwater for domestic and irrigation uses. Civil Eng J 5(9):1877–1892
Hussain MH, Singhal DC, Joshi H, Kumar S (2006) Assessment of groundwater vulnerability in a tropical alluvial interfluve, India. Bhu-Jal News J 21:31–43
Jarray H, Zammouri M, Ouessar M, Hamzaoui-Azaza F, Barbieri M, Zerrim A, Soler A, Yahyaoui H (2017) Groundwater vulnerability based on GIS approach: case study of Zeuss-Koutine aquifer, South-Eastern Tunisia. Geofis Int 56-2:157–172
Kansoh R, Abd-El-Mooty M, Abd-El-Baky R (2020) Computing the water budget components for lakes by using meteorological data. Civil Eng J 6(7):1255–1265. https://doi.org/10.28991/cej-2020-03091545
Khemiri S, Khnissi A, Alaya MB, Saidi S, Zargouni F (2013) Using GIS for the comparison of intrinsic parametric methods assessment of groundwater vulnerability to pollution in scenarios of semi-arid climate. The Case of Foussana Groundwater in the Central of Tunisia. J Wat Resour Prot 5:835–845. https://doi.org/10.4236/jwarp.2013.58084
Kuisi MA, El-Naqa A, Hammouri N (2006) Vulnerability mapping of shallow groundwater aquifer using SINTACS model in the Jordan Valley area, Jordan. Environ Geol 50:645–650
Panagopoulos G, Antonakos A, Lambrakis N (2005) Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS. Hydrogeol J 14:894–911
Rahman A (2008) A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Appl Geogr 28:32–53
Sener E, Sener S, Davraz A (2009) Assessment of aquifer vulnerability based on GIS and DRASTIC methods: a case study of the Senirkent-Uluborlu Basin (Isparta, Turkey). Hydrogeol J 17:2023–2035
Shrestha S, Semkuyu DJ, Pandey VP (2016) Assessment of groundwater vulnerability and risk to pollution in Kathmandu Valley. (Nepal). Sci Total Environ 556:23–35
Twarakavi NKC, Kaluarachchi JJ (2006) Sustainability of ground water quality considering land use changes and public health risks. J Environ Manag 81:405–419
Uma KO (2003) Hydrogeology of the perched aquifer systems in the hilly terrains of Nsukka town, Enugu State, Nigeria. Wat Resour J 14:85–92
Van Stempvoort D, Evert L, Wassenaar L (1993) Aquifer vulnerability index: a GIS compactable method for groundwater vulnerability mapping. Can Water Resour J 18:25–37
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares 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/s12517-021-08944-w
About this article
Cite this article
Yu, X. RETRACTED ARTICLE: Rainfall erosion characteristics of loess slope based on big data and internal financial control of enterprises. Arab J Geosci 14, 1589 (2021). https://doi.org/10.1007/s12517-021-07960-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-07960-0