The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China

doi:10.1007/s11707-014-0465-4

Front. Earth Sci.

2015, Vol. 9

Issue (2) : 342-354 https://doi.org/10.1007/s11707-014-0465-4

RESEARCH ARTICLE

The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China

Yue LIU^1,²(

), Qiuming CHENG^1,³, Qinglin XIA², Xinqing WANG²

¹. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
². Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China
³. Department of Earth and Space Science and Engineering, Department of Geography, York University, Toronto M3J1P3, Canada

Download: PDF(3182 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In this study, the evidential belief functions (EBFs) were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers (e.g., geological, geochemical, and geophysical) related to tungsten polymetallic deposits were extracted from a multi-source geospatial database. The relationships between evidential layers and the target deposits were quantified using EBFs model. Four EBF maps (belief map, disbelief map, uncertainty map, and plausibility map) are generated by integrating seven evidential layers which provide meaningful interpretations for tungsten polymetallic potential. On the final predictive map, the study area was divided into three target zones of high potential, moderate potential, and low potential areas, among which high potential and moderate potential areas accounted for 17.8% of the total area, containing 81% of the total deposits. To evaluate the success rate accuracy, the receiver operating characteristic (ROC) curves and the area under the curves (AUC) for the belief map were calculated. The area under the curve is 0.81 which indicates that the capability for correctly classifying the areas with existing mineral deposits is satisfactory. The results of this study indicate that the EBFs were effectively used for mapping mineral potential and for managing uncertainties associated with evidential layers.

Keywords Dempster-Shafer theory of evidence GIS uncertainty tungsten polymetallic deposit ROC curve

Corresponding Author(s): Yue LIU

Online First Date: 12 December 2014 Issue Date: 30 April 2015

Cite this article:

Yue LIU,Qiuming CHENG,Qinglin XIA, et al. The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China[J]. Front. Earth Sci., 2015, 9(2): 342-354.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0465-4
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I2/342

Fig.1 Simplified geologic map of the Nanling belt, South China, showing tungsten polymetallic deposits.

Fig.2 Maps in the spatial database for mineral potential mapping, showing tungsten polymetallic deposits. (a) NE trending faults; (b) NW trending faults; (c) EW trending faults; (d) SN tending faults.

Fig.3 Maps in the spatial database for mineral potential mapping, showing tungsten polymetallic deposits. (a) geochemical element association; (b) lithostratigraphic contacts; (c) aeromagnetic.

Evidential layer	Class description	N(C_ij)–N(C_ij∩D)	N(C_ij∩D)	Bel	Dis	Unc
Aeromagnetic	0–10%	18,749	15	0.072	0.100	0.828
Aeromagnetic	10%–20%	18,743	20	0.099	0.100	0.801
Aeromagnetic	20%–30%	18,747	17	0.083	0.100	0.817
Aeromagnetic	30%–40%	18,742	23	0.116	0.100	0.784
Aeromagnetic	40%–50%	18,741	23	0.116	0.100	0.784
Aeromagnetic	50%–60%	18,737	27	0.139	0.100	0.761
Aeromagnetic	60%–70%	18,862	28	0.144	0.100	0.756
Aeromagnetic	70%–80%	18,703	19	0.094	0.100	0.806
Aeromagnetic	80%–90%	18,701	20	0.099	0.100	0.801
Aeromagnetic	90%–100%	18,713	8	0.037	0.100	0.863
Contacts	0–1?km	41,942	133	0.671	0.125	0.204
Contacts	1–2?km	15,603	23	0.139	0.125	0.736
Contacts	2–3?km	14,553	12	0.074	0.125	0.801
Contacts	3–4?km	10,660	8	0.067	0.125	0.808
Contacts	4–5?km	11,609	3	0.022	0.125	0.852
Contacts	5–6?km	8,211	1	0.011	0.125	0.864
Contacts	6–7?km	7,339	0	0.000	0.125	0.875
Contacts	>7?km	77,521	20	0.015	0.125	0.859
F3	0–10%	17,689	1	0.003	0.100	0.896
F3	10%–20%	20,148	2	0.006	0.100	0.894
F3	20%–30%	21,295	3	0.009	0.100	0.891
F3	30%–40%	18,992	9	0.030	0.100	0.870
F3	40%–50%	18,884	9	0.030	0.100	0.870
F3	50%–60%	19,287	10	0.033	0.100	0.867
F3	60%–70%	19,399	19	0.065	0.100	0.835
F3	70%–80%	17,792	27	0.106	0.100	0.794
F3	80%–90%	17,080	30	0.126	0.100	0.774
F3	90%–100%	16,872	90	0.592	0.100	0.309
NE trending fault	0–1?km	9,742	18	0.100	0.077	0.823
NE trending fault	1–2?km	7,235	18	0.136	0.077	0.787
NE trending fault	2–3?km	9,276	19	0.112	0.077	0.811
NE trending fault	3–4?km	8,890	15	0.090	0.077	0.833
NE trending fault	4–5?km	10,712	22	0.113	0.077	0.810
NE trending fault	5–6?km	8,866	17	0.104	0.077	0.819
NE trending fault	6–7?km	8,239	8	0.050	0.077	0.873
NE trending fault	7–8?km	8,766	13	0.079	0.077	0.845
NE trending fault	>8?km	8,367	4	0.024	0.077	0.899
NW trending fault	0–1?km	7,838	10	0.067	0.077	0.856
NW trending fault	1–2?km	6,168	5	0.042	0.077	0.881
NW trending fault	2–3?km	5,568	7	0.066	0.077	0.857
NW trending fault	3–4?km	87,771	44	0.018	0.077	0.905
NW trending fault	4–5?km	64,241	111	0.347	0.111	0.542
NW trending fault	5–6?km	26,040	29	0.152	0.111	0.737
NW trending fault	6–7?km	24,903	23	0.123	0.111	0.766
NW trending fault	>7?km	15,668	14	0.120	0.111	0.769
SN trending fault	0–1?km	15,756	10	0.083	0.111	0.806
SN trending fault	1–2?km	8,975	3	0.044	0.111	0.845
SN trending fault	2–3?km	6,981	3	0.057	0.111	0.832
SN trending fault	3–4?km	6,505	2	0.041	0.111	0.848
SN trending fault	4–5?km	18,369	5	0.034	0.111	0.855
SN trending fault	5–6?km	20,455	37	0.191	0.125	0.685
SN trending fault	6–7?km	12,117	16	0.129	0.125	0.746
SN trending fault	7–8?km	15,054	32	0.224	0.125	0.651
SN trending fault	8–9?km	12,245	9	0.069	0.125	0.806
SN trending fault	9–10?km	14,686	28	0.197	0.125	0.678
SN trending fault	>10?km	10,579	8	0.072	0.125	0.803
EW trending fault	0–1?km	9,519	7	0.070	0.125	0.805
EW trending fault	1–2?km	92,783	63	0.048	0.125	0.827
EW trending fault	2–3?km	19,288	34	0.142	0.091	0.768
EW trending fault	3–4?km	12,161	11	0.066	0.091	0.843
EW trending fault	4–5?km	13,858	31	0.182	0.091	0.727
EW trending fault	5–6?km	12,862	20	0.120	0.091	0.790
EW trending fault	6–7?km	14,804	24	0.126	0.091	0.783
EW trending fault	7–8?km	11,852	8	0.049	0.091	0.860
EW trending fault	8–9?km	10,688	11	0.076	0.091	0.833
EW trending fault	9–10?km	11,044	4	0.026	0.091	0.883
EW trending fault	10–11?km	10,334	19	0.143	0.091	0.767
EW trending fault	11–12?km	9,498	5	0.038	0.091	0.871
EW trending fault	>12?km	61,049	33	0.032	0.091	0.877

Tab.1 Results of EBFs calculated from seven evidential layers

Fig.4 Integration results using EBFs. (a) the belief map; (b) the disbelief map; (c) the uncertainty map; (d) the plausibility map.

Fig.5 (a) Variations of cumulative tungsten polymetallic deposits with belief probabilities; (b) Variations of cumulative tungsten polymetallic deposits with cumulative percent of study area.

Fig.6 Final tungsten polymetallic potential map based on the belief map.

Fig.7 Success rate curve for the belief map.

1	M Abedi, GH Norouzi, N Fathianpour (2013) Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping. Int J Appl Earth Obs Geoinformat, 21: 556–567 https://doi.org/doi.org/10.1016/j.jag.2012.07.012
2	F P Agterberg (1992). Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenewable Resources, 1(1): 39–50 https://doi.org/10.1007/BF01782111
3	F P Agterberg, G F Bonham–Carter, Q Cheng, D F Wright (1993). Weights of evidence model and weighted logistic regression in mineral potential mapping. In: J C Davis et al. (eds.). Computers in Geology. New York: Oxford University Press, 13–32
4	F P Agterberg, Q Cheng (2002). Conditional independence test of weights–of– evidence modeling. Nat Resour Res, 11(4): 249–255 https://doi.org/10.1023/A:1021193827501
5	P An, W M Moon, G F Bonham–Carter (1992). On knowledge–based approach to integrating remote sensing, geophysical and geological information. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), 34–38
6	P An, W M Moon, G F Bonham–Carter (1994a). An objectoriented knowledge representation structure for exploration data integration. Nat Resour Res, 3(2): 132–145 https://doi.org/10.1007/BF02286438
7	P An, W M Moon, G F Bonham–Carter (1994b). Uncertainty management integration of exploration data using the belief function. Nat Resour Res, 3(1): 60–71 https://doi.org/10.1007/BF02261716
8	P Behnia (2007). Application of radial basis functional link networks to exploration for proterozoic mineral deposits in central Iran. Nat Resour Res, 16(2): 147–155 https://doi.org/10.1007/s11053-007-9036-7
9	G F Bonham–Carter, F P Agterberg, D F Wright (1989). Weights of evidence modelling: a new approach to mapping mineral potential. In Agterberg F P and Bonham–Carter G F eds. Statistical Applications in the Earth Sciences: Geol. Survey Canada Paper 89-9, 171–183
10	E J M Carranza, M Hale (2001a). Logistic regression for geologically constrained mapping of gold potential, Baguio District. Phil.Exploration and Mining Geol., 10(3): 165–175 https://doi.org/10.2113/0100165
11	E J M Carranza, M Hale (2001b). Geologically constrained fuzzy mapping of gold mineralization potential, Baguio District, Philippines. Nat Resour Res, 10(2): 125–136 https://doi.org/10.1023/A:1011500826411
12	E J M Carranza, M Hale (2003). Evidential belief functions for data–driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev, 22(1–2): 117–132 https://doi.org/10.1016/S0169-1368(02)00111-7
13	E J M Carranza (2004). Weights of evidence modeling of mineral potential: A case study using small number of prospects, Abra, Philippines. Nat Resour Res, 13(3): 173–187 https://doi.org/10.1023/B:NARR.0000046919.87758.f5
14	E J M Carranza, T Woldai, E M Chikambwe (2005). Application of data–driven evidential belief functions to potential mapping for aquamarine–bearing pegmatites, Lundazi district Zambia. Nat Resour Res, 14(1): 47–63 https://doi.org/10.1007/s11053-005-4678-9
15	E J M Carranza, M Hale, C Faassen (2008a). Selection of coherent deposit–type locations and their application in data–driven mineral prospectivity mapping. Ore Geol Rev, 33(3–4): 536–558 https://doi.org/10.1016/j.oregeorev.2007.07.001
16	E J M Carranza, F J A Van Ruitenbeek, C A Hecker, M Van der Meijde, F D Van derMeer (2008b). Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs Geoinf, 10(3): 374–387 https://doi.org/10.1016/j.jag.2008.02.008
17	E J M Carranza (2014). Data-driven evidential belief modeling of mineral potential using few Prospects and evidence with missing values. Nat Resour Res
18	Y Chen, R Pei, H Zhang (1990). Mineral deposit of nonferrous metal and rare metal associated with Mesozoic granite in the Nanling region. Bulletin of the Chinese Academy of Geological Sciences, 20(1): 79–85 (in Chinese)
19	Q Cheng, F P Agterberg (1999). Fuzzy weights of evidence method and its application in mineral potential mapping. Nat Resour Res, 8(1): 27–35 https://doi.org/10.1023/A:1021677510649
20	Q Cheng (2012). Application of a newly developed boost weights of evidence model (BoostWofE) for mineral resource quantitative assessments. Journal of Jilin University, 42(6): 1976–1984 (Earth Science Edition)
21	Q Chi, X Wang, S Xu (2012). Temporal and spatial distribution of tungsten and tin in South China Continent. Earth Sci Front, 19(3): 70–83 (in Chinese with English abstract)
22	C F Chung, A Fabbri (1993). The representation of geoscience information for data integration. Nonrenewable Resources, 2(2): 122–139 https://doi.org/10.1007/BF02272809
23	C F Chung, A G Fabbri (1999). Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sensing, 65(12): 1389–1399
24	C F Chung, A Fabbri (2003). Validation of spatial prediction models for landslide hazard mapping. Nat Hazards, 30(3): 451–472 https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
25	A P Dempster (1967). Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat, 38(2): 325–339 https://doi.org/10.1214/aoms/1177698950
26	A P Dempster (1968). A generalization of Bayesian inference. J R Stat Soc, B, 30(2): 205–247
27	P S Hsieh, C H Chen, H J Yang, C Y Lee (2008). Petrogenesis of the Nanling Mountains granites from South China: Constraints from systematic apatite geochemistry and whole-rock geochemical and Sr–Nd isotope compositions. J Asian Earth Sci, 33(5–6): 428–451 https://doi.org/10.1016/j.jseaes.2008.02.002
28	R Hu, X Bi, G Jiang, H Chen, J Peng, Y Qi, L Wu, W Wei (2012). Mantle-derived noble gases in ore-forming fluids of the granite-related Yaogangxian tungsten deposit, Southeastern China. Miner Depos, 47(6): 623–632 https://doi.org/10.1007/s00126-011-0396-x
29	R Hu, M Zhou (2012). Multiple Mesozoic mineralization events in South China—an introduction to the thematic issue. Miner Depos, 47(6): 579–588 https://doi.org/10.1007/s00126-012-0431-6
30	R Hua, P Chen, W Zhang, J Yao, J Lin, Z Zhang, S Gu, X Liu, H Qi(2005). Metallogeneses related to Mesozoic granitoids in the Nanling Range, and their geodynamic settings. Acta Geologica Sinica (English Edition), 79(6): 801–811
31	E P Leite, C R D S Filho (2009) Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Caraja’s Mineral Province, Brazil. Comput Geosci, 35: 675–687 https://doi.org/10.1016/j.cageo.2008.05.003
32	S Lee, N T Dan (2005). Probabilistic landslide susceptibility mapping on the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environmental Geology, 48(6): 778–787 https://doi.org/10.1007/s00254-005-0019-x
33	S Lee, B Pradhan (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1): 33–41 https://doi.org/10.1007/s10346-006-0047-y
34	B Li (2011). Synchronization theory and tungsten-polymetallicmineralization distribution in the Qianlishan–Qitianling area, Southern Hunan. J. Earth Sci, 22: 726–736 https://doi.org/10.1007/s12583-011-0223-4
35	X Li, W Li, X Wang, Q Li, Y Liu, G Tang (2009). Role of mantle-derived magma in genesis of early Yanshanian granites in the Nanling Range, South China: in situ zircon Hf-O isotopic constraints. Science China Ser D, 52(9): 1262–1278 https://doi.org/10.1007/s11430-009-0117-9
36	N Liu, C Yu (2011). Analysis of onset and development of ore formation in Dajishan tungsten ore area, Jiangxi Province, China. J. Earth Sci, 22(1): 67–74 https://doi.org/10.1007/s12583-011-0158-9
37	Y Liu, Q Cheng, Q Xia, X Wang (2013a). Application of singularity analysis for mineral potential identification using geochemical data — a case study: Nanling W–Sn–Mo polymetallic metallogenic belt, South China. J Geochem Explor, 134: 61–72 https://doi.org/10.1016/j.gexplo.2013.08.006
38	Y Liu, Q Xia, Q Cheng, X Wang (2013b). Application of singularity theory and logistic regression model for tungsten polymetallic potential mapping. Nonlinear Process Geophys, 20(4): 445–453 https://doi.org/10.5194/npg-20-445-2013
39	Y Liu, Q Cheng, Q Xia, X Wang (2014a). Mineral potential mapping for tungsten polymetallic deposits in the Nanling metallogenic belt, South China. J. Earth Sci, 25(4): 689–700 https://doi.org/10.1007/s12583-014-0466-y
40	Y Liu, Q Cheng, Q Xia, X Wang (2014b). Identification of REE mineralization-related geochemical anomalies using fractal/multifractal methods in the Nanling belt, South China. Environ Earth Sci
41	Y Liu, Q Cheng, Q Xia, X Wang (2014c). Multivariate analysis of stream sediment data from Nanling metallogenic belt, South China. Geochem Explor Environ Anal,
42	X Luo, R Dimitrakopoulos (2003). Data-driven fuzzy analysis in quantitative mineral resource assessment. Comput Geosci, 29(1): 3–13 https://doi.org/10.1016/S0098-3004(02)00078-X
43	J Mao, G Xie, C Guo, Y Chen (2007). Large-scale tungsten-tin mineralization in the Nanling region South China: Metallogenic ages and corresponding geodynamic processes. Acta Petrol Sin, 23(10): 2329–2338 (in Chinese with English abstract)
44	J Mao, G Xie, Y Cheng, Y Chen (2009). Mineral deposit models of Mesozoic ore deposits in South China. Geological Review, 55(3): 347–354 (in Chinese with English abstract)
45	W M Moon (1989). Integration of remote sensing and geological/geophysical data using Dempster-Shafer approach. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), 838–841
46	V Nykänen, V J Ojala (2007). Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the Northern Fennoscandian Shield, Finland. Nat Resour Res, 16(2): 85–92 https://doi.org/10.1007/s11053-007-9046-5
47	H J Oh, S Lee (2010). Application of artificial neural network for gold-silver deposits potential mapping: A case study of Korea. Nat Resour Res, 19(2): 103–124 https://doi.org/10.1007/s11053-010-9112-2
48	R Pei, Y Wang, H Wang (2009). Ore-forming specialty of the tectono- magmatic zone in Nanling region and its emplacement dynamics for metallogenic series of W–Sn polymetallic deposits. Geology in China, 36(3): 483–489 (in Chinese with English abstract)
49	J Peng, M Zhou, R Hu, N Shen, S Yuan, X Bi, A Du, W Qu (2006). Precise molybdenite Re–Os and mica Ar–Ar dating of the Mesozoic Yaogangxian tungsten deposit, central Nanling district, South China. Miner Depos, 41(7): 661–669 https://doi.org/10.1007/s00126-006-0084-4
50	A Porwal, E J M Carranza, M Hale (2003a). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Nat Resour Res, 12(1): 1–25 https://doi.org/10.1023/A:1022693220894
51	A Porwal, E J M Carranza, M Hale (2003b). Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Nat Resour Res, 12(3): 155–171 https://doi.org/10.1023/A:1025171803637
52	A Porwal, I González-Álvarez, V Markwitz, T C McCuaig, A Mamuse (2010). Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geol Rev, 38(3): 184–196 https://doi.org/10.1016/j.oregeorev.2010.04.002
53	B Qin (1987). A geological interpretation on the regional gravity and magnetic anomalies in Nanling Area. Hunan Geology, 1: 1–15 (in Chinese with English abstract)
54	G Shafer (1976). A Mathematical Theory of Evidence. Princeton: Princeton Univ. Press, 297
55	X Shu, X Wang, S Tao, X Xu, M Dai (2011). Trace elements, U–Pb ages and Hf isotopes of zircons from Mesozoic granites in the western Nanling Range, South China: Implications for petrogenesis and W–Sn mineralization. Lithos, 127(3–4): 468–482 https://doi.org/10.1016/j.lithos.2011.09.019
56	Z Yuan, C Wu, L Xu, Y Ni (1993). The distribution of trace elements in granitoids in the Nanling Region of China. Chinese Journal of Geochemistry, 12(3): 193–205 https://doi.org/10.1007/BF02843359

[1]	Weisheng HOU, Qiaochu YANG, Xiuwen CHEN, Fan XIAO, Yonghua CHEN. Uncertainty analysis and visualization of geological subsurface and its application in metro station construction[J]. Front. Earth Sci., 2021, 15(3): 692-704.
[2]	Emre ÇOLAK, Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
[3]	Evangelin Ramani SUJATHA. A spatial model for the assessment of debris flow susceptibility along the Kodaikkanal-Palani traffic corridor[J]. Front. Earth Sci., 2020, 14(2): 326-343.
[4]	Shifa MA, Feng LIU, Chunlei MA, Xuemin OUYANG. Integrating logistic regression with ant colony optimization for smart urban growth modelling[J]. Front. Earth Sci., 2020, 14(1): 77-89.
[5]	Mohammad Saeid MIRAKHORLO, Majid RAHIMZADEGAN. Evaluating estimated sediment delivery by Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Distributed (SEDD) in the Talar Watershed, Iran[J]. Front. Earth Sci., 2020, 14(1): 50-62.
[6]	Guiyun ZHOU, Hongqiang WEI, Suhua FU. A fast and simple algorithm for calculating flow accumulation matrices from raster digital elevation[J]. Front. Earth Sci., 2019, 13(2): 317-326.
[7]	Grey S. NEARING, Hoshin V. GUPTA. Ensembles vs. information theory: supporting science under uncertainty[J]. Front. Earth Sci., 2018, 12(4): 653-660.
[8]	S.R. FASSNACHT, K.S.J. BROWN, E.J. BLUMBERG, J.I. LÓPEZ MORENO, T.P. COVINO, M. KAPPAS, Y. HUANG, V. LEONE, A.H. KASHIPAZHA. Distribution of snow depth variability[J]. Front. Earth Sci., 2018, 12(4): 683-692.
[9]	Igor Appel. Uncertainty in satellite remote sensing of snow fraction for water resources management[J]. Front. Earth Sci., 2018, 12(4): 711-727.
[10]	D.M. HULTSTRAND, S.R. FASSNACHT. The sensitivity of snowpack sublimation estimates to instrument and measurement uncertainty perturbed in a Monte Carlo framework[J]. Front. Earth Sci., 2018, 12(4): 728-738.
[11]	Xiaoping LIU, Shuli CHEN, Li ZHUO, Jun LI, Kangning HUANG. Multi-sensor image registration by combining local self-similarity matching and mutual information[J]. Front. Earth Sci., 2018, 12(4): 779-790.
[12]	Bilaşco ŞTEFAN, Roşca SANDA, Fodorean IOAN, Vescan IULIU, Filip SORIN, Petrea DĂNUŢ. Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models[J]. Front. Earth Sci., 2018, 12(2): 311-324.
[13]	Chong PENG, Hao XIAO, Yu LIU, Jingjing ZHANG. Economic structure and environmental quality and their impact on changing land use efficiency in China[J]. Front. Earth Sci., 2017, 11(2): 372-384.
[14]	C. DAI,W. SUN,Q. TAN,Y. LIU,W.T. LU,H.C. GUO. Risk management for sulfur dioxide abatement under multiple uncertainties[J]. Front. Earth Sci., 2016, 10(1): 87-107.
[15]	Jianqi ZHUANG,Jianbing PENG,Javed IQBAL,Tieming LIU,Na LIU,Yazhe LI,Penghui MA. Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi’an Region, Shaanxi Province, China[J]. Front. Earth Sci., 2015, 9(3): 449-462.

Viewed

Full text

Abstract

Cited

Shared

Discussed