Application of Multifactor Hierarchical Interpolation Technique Based on Improved Random Forest Model in Spatial Distribution of Selenium in Soils

  • Lili JIA ,
  • Tingting LI ,
  • Xin ZHU ,
  • Longke YI ,
  • Siliang LUO
Expand
  • Geological Investigation Institute of Guangdong Province, Guangzhou 510080, China
JIA Lili, Senior engineer, research areas include geochemical exploration, agricultural geological survey and research. E-mail: lichia@126.com

Received date: 2023-06-18

  Revised date: 2023-10-18

  Online published: 2023-12-26

Supported by

the National Nature Science Foundation of China(U1911202);Guangdong Geological Exploration and Urban Geological Project(2023-25);Zhanjiang Municipal Finance Project(CLZ0121ZJ01ZC00)

Abstract

The spatial distribution of geochemical elements is complex, and traditional regional geochemical surveys and analyses consider only spatial information elements, which do not reflect the heterogeneity of other elements. Therefore, this study used hierarchical regionalization labels, such as geological, geographical, spatial, and ecological elements along with anthropogenic activities, as covariates of the machine learning model to predict the spatial distribution of selenium elements. By comparing Random Forest (RF), extreme gradient lifting tree (XGboost), Feature Contribution-Random Forest (FC-RF), Deep learning Neural Network (DNN) models, and traditional spatial interpolation techniques; this study showed that the FC-RF model has higher precision in selenium prediction, which indicates that multilevel interpolation technology based on the FC-RF model is more feasible for predicting the spatial distribution of soil selenium. Based on the predicted results, a Bayesian Laser Interferometer Space Antenna (LISA) spatial correlation analysis was conducted on the spatial distribution of selenium on Naozhou Island, indicating that the key factors affecting the spatial distribution of selenium are basic geological conditions and anthropogenic activities. This study further expands the service application dimensions of geochemical data, thus making the use of soil geochemical data more accurate and scientific.

Cite this article

Lili JIA , Tingting LI , Xin ZHU , Longke YI , Siliang LUO . Application of Multifactor Hierarchical Interpolation Technique Based on Improved Random Forest Model in Spatial Distribution of Selenium in Soils[J]. Advances in Earth Science, 2023 , 38(12) : 1259 -1270 . DOI: 10.11867/j.issn.1001-8166.2023.081

References

1 HU Q, CHEN L C, XU J, et al. Determination of selenium concentration in rice and the effect of foliar application of Se-enriched fertiliser or sodium selenite on the selenium content of rice[J]. Journal of the Science of Food and Agriculture, 2002, 82: 869-872.
2 QIAO Xinxing, CHAO Xu, REN Rui, et al. Research, development and utilization of selenium-rich soil of Shaanxi: a case study of Sanyuan-Yanliang area[J]. Geophysical and Geochemical Exploration, 2021, 45(1): 230-238.
2 乔新星, 晁旭, 任蕊, 等. 陕西关中富硒土壤研究及开发利用: 以三原—阎良地区为例[J]. 物探与化探, 2021, 45(1): 230-238.
3 WANG Zhiqiang, YANG Jianfeng, SHI Tianchi. A preliminary study of Se-rich soil in the Shizuishan area, Ningxia and its potential for application[J]. Geophysical and Geochemical Exploration, 2023, 47(1): 228-237.
3 王志强, 杨建锋, 石天池. 宁夏石嘴山地区富硒土壤及其利用前景[J]. 物探与化探, 2023, 47(1): 228-237.
4 YUAN Hongwei, CHEN Jiangjun, GUO Tengda, et al. Geochemical characteristics and influencing factors of Se-rich soils in Langshan and Xinhua towns, Linhe district, Bayannur City[J]. Geology and Exploration, 2022, 58(5): 1 027-1 041.
4 袁宏伟, 陈江均, 郭腾达, 等. 巴彦淖尔市临河区狼山镇和新华镇一带富硒土壤地球化学特征及影响因素[J]. 地质与勘探, 2022, 58(5): 1 027-1 041.
5 LIU Jian, WANG Yifan, LIN Zhongyang, et al. Distribution, sources and ecological effects of selenium in topsoil of cultivated land in Jiande City, Zhejiang Province[J]. Geoscience, 2022, 36(3): 953-962.
5 刘健, 汪一凡, 林钟扬, 等. 浙江建德市耕地表层土壤硒分布、来源及生态效应[J]. 现代地质, 2022, 36(3): 953-962.
6 LI Tingting, JIA Lili, ZHU Xin, et al. Geochemical evaluation of the land quality and classification of the selenium-enriched soils in Chengyue area, Leizhou peninsula[J]. Earth and Environment, 2022, 50(4): 481-489.
6 李婷婷, 贾黎黎, 朱鑫, 等. 雷州半岛富硒区土地质量地球化学评价及其利用区划研究: 以城月地区为例[J]. 地球与环境, 2022, 50(4): 481-489.
7 STEIN A, HOOGERWERF M, BOUMA J. Use of soil-map delineations to improve (Co-) Kriging of point data on moisture deficits[J]. Geoderma, 1988, 43(2/3): 163-177.
8 HAN Z H, G?RTZ S. Hierarchical kriging model for variable-fidelity surrogate modeling[J]. AIAA Journal, 2012, 50(9): 1 885-1 896.
9 LIN Y P, CHENG B Y, CHU H J, et al. Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods[J]. Geoderma, 2011, 163(3/4): 275-282.
10 YOUNG M T, BECHLE M J, SAMPSON P D, et al. Satellite-based NO2 and model validation in a national prediction model based on universal kriging and land-use regression[J]. Environmental Science & Technology, 2016, 50(7): 3 686-3 694.
11 TAN Z, YANG Q, ZHENG Y. Machine learning models of groundwater arsenic spatial distribution in Bangladesh: influence of Holocene sediment depositional history[J]. Environmental Science & Technology, 2020, 54(15): 9 454-9 463.
12 HENGL T, NUSSBAUM M, WRIGHT M N, et al. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables[J]. PeerJ, 2018. DOI:10.7717/peerj.5518 .
13 DEMYANOV V, KANEVSKY M, CHERNOV S, et al. Neural network residual Kriging application for climatic data[J].Journal of Geographic Information and Decision Analysis,1998,2:215-232.
14 ZHAO W H, MA J, LIU Q Y, et al. Accurate prediction of soil heavy metal pollution using an improved machine learning method: a case study in the Pearl River Delta, China[J]. Environmental Science & Technology, 2023, 57(46): 17 751-17 761.
15 HU Y A, CHENG H F. Application of stochastic models in identification and apportionment of heavy metal pollution sources in the surface soils of a large-scale region[J]. Environmental Science & Technology, 2013, 47(8): 3 752-3 760.
16 ZHANG H, YIN A J, YANG X H, et al. Use of machine-learning and receptor models for prediction and source apportionment of heavy metals in coastal reclaimed soils[J]. Ecological Indicators, 2021, 122. DOI:10.1016/j.ecolind.2020.107233 .
17 BREIMAN L. Random forests[J]. Machine Learning, 2001, 45: 5-32.
18 ZHANG H, WU P B, YIN A J, et al. Prediction of soil organic carbon in an intensively managed reclamation zone of Eastern China: a comparison of multiple linear regressions and the random forest model[J]. Science of the Total Environment, 2017, 592: 704-713.
19 CHEN T Q, GUESTRIN C. XGboost: a scalable tree boosting system[C]// Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016: 785-794.
20 WONG P Y, LEE H Y, CHEN Y C, et al. Using a land use regression model with machine learning to estimate ground level PM2.5 [J]. Environmental Pollution, 2021, 277. DOI:10.1016/j.envpol.2021.116846 .
21 XIA Weiping, TAN Jianan. A comparative study of selenium content in Chinese rocks[J]. Acta Scientiae Circumstantiae, 1990, 10(2): 125-131.
21 夏卫平, 谭见安. 中国一些岩类中硒的比较研究[J].环境科学学报, 1990, 10(2): 125-131.
22 QIN Jianxun, FU Wei, ZHENG Guodong, et al. Selenium distribution in surface soil layer of Karst area of Guangxi and its affecting factors: a case study of Wuming County[J]. Acta Pedologica Sinica, 2020, 57(5): 1 299-1 310.
22 覃建勋, 付伟, 郑国东, 等. 广西岩溶区表层土壤硒元素分布特征与影响因素探究: 以武鸣县为例[J]. 土壤学报, 2020, 57(5): 1 299-1 310.
23 WEI Zhenshan, TU Qijun, TANG Shuhong, et al. A discussion on the geochemical features and origin of selenium-rich soil on the northern slope of the Tianshan Mountains from Urumqi to Shawan County[J]. Geophysical and Geochemical Exploration, 2016, 40(5): 893-898.
23 魏振山, 涂其军, 唐蜀虹, 等. 天山北坡乌鲁木齐至沙湾地区富硒土壤地球化学特征及成因探讨[J]. 物探与化探, 2016, 40(5): 893-898.
24 LI Yigen, DONG Yanxiang, ZHENG Jie, et al. Selenium: abundant soil survey and assessment in Zhejiang[J]. Quaternary Sciences, 2005, 25(3): 323-330.
24 郦逸根, 董岩翔, 郑洁, 等. 浙江富硒土壤资源调查与评价[J]. 第四纪研究, 2005, 25(3): 323-330.
25 ZHOU Mo, CHEN Guoguang, ZHANG Ming, et al. Geochemical characteristics and influencing factors of selenium in soils of South Jiangxi Province: a typical area of Qingtang-Meijiao[J]. Geoscience, 2018, 32(6): 1 292-1 301.
25 周墨, 陈国光, 张明, 等. 赣南地区土壤硒元素地球化学特征及其影响因素研究: 以青塘—梅窖地区为例[J]. 现代地质, 2018, 32(6): 1 292-1 301.
26 LIU Daorong, XU Hong, ZHOU Yi, et al. Characteristics and genetic analysis of selenium-rich soil in Changshan County, western Zhejiang Province[J]. Geophysical and Geochemical Exploration, 2019, 43(3): 658-666.
26 刘道荣, 徐虹, 周漪, 等. 浙西常山地区富硒土壤特征及成因分析[J]. 物探与化探, 2019, 43(3): 658-666.
27 HONG Wanhua, SU Te, TU Feifei, et al. Study on the distribution and influencing factors of selenium based on the geochemical method of land quality: a case study of Tongren area[J]. Metal Mine, 2021(12): 160-168.
27 洪万华, 苏特, 涂飞飞, 等. 基于土地质量地球化学方法的硒元素分布规律和影响因素研究: 以铜仁地区为例[J]. 金属矿山, 2021(12): 160-168.
28 CAO Ronghao. Study on selenium content of surface soils in Longhai, Fujian and its influencing factors[J]. Rock and Mineral Analysis, 2017, 36(3): 282-288.
28 曹容浩. 福建省龙海市表层土壤硒含量及影响因素研究[J]. 岩矿测试, 2017, 36(3): 282-288.
29 CHEN Chunliang, BAO Kaiqiang, WANG Mengying, et al. Effects of vegetation removal on soil organic matter and nutrients in an erosive environment[J]. Research of Soil and Water Conservation, 2022, 29(5): 131-136.
29 陈春良, 鲍凯强, 王梦莹, 等. 植被去除对侵蚀环境土壤有机质和养分的影响[J]. 水土保持研究, 2022, 29(5): 131-136
30 HOBBIE S E. Effects of plant species on nutrient cycling[J]. Trends in Ecology & Evolution, 1992, 7(10): 336-339.
31 GUSTAFSSON J P, JACKS G, STEGMANN B, et al. Soil acidity and adsorbed anions in Swedish forest soils—long-term changes[J]. Agriculture, Ecosystems & Environment, 1993, 47(2): 103-115.
32 GUSTAFSSON J P, JOHNSSON L. Selenium retention in the organic matter of Swedish forest soils[J]. Journal of Soil Science, 1992, 43(3): 461-472.
33 SHANG Jingmin, LUO Wei, WU Guanghong, et al. Spatial distribution of Se in soils from different land use types and its influencing factors within the Yanghe watershed, China[J]. Environmental Science, 2015, 36(1): 301-308.
33 商靖敏, 罗维, 吴光红, 等. 洋河流域不同土地利用类型土壤硒(Se)分布及影响因素[J]. 环境科学, 2015, 36(1): 301-308.
34 YU T, YANG Z F, LV Y Y, et al. The origin and geochemical cycle of soil selenium in a Se-rich area of China[J]. Journal of Geochemical Exploration, 2014, 139: 97-108.
35 HOU Shaofan, LI Dezhu, WANG Lizhen, et al. The differentiation characteristics of soil sel-enium in warm-temperate geographic lands-cape[J]. Acta Geographica Sinica, 1992, 47(1): 31-39.
35 侯少范, 李德珠, 王丽珍, 等. 暖温带地理景观中土壤硒的分异特征[J]. 地理学报, 1992, 47(1): 31-39.
36 ZHANG Miaomei. Study on seawater intrusion in Naozhou Island, Guangdong Province[J]. Underground Water, 2018, 40(5):138-139, 159.
36 张妙美. 广东省硇洲岛海水入侵研究[J]. 地下水, 2018, 40(5):138-139, 159.
Outlines

/