Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 281–294 | Cite as

Multivariate statistical techniques for prediction of tree and shrub species plantation using soil parameters

  • Govind Eknath KulkarniEmail author
  • Aniket Avinash MuleyEmail author
  • Nilesh Kailasrao Deshmukh
  • Parag Upendra Bhalchandra
Original Article


In this paper, an attempt has been made to find out the conceptual framework of urban greenery planning and resolve multifaceted environmental problems. The decision making for greenery space planning may be supported by applying geographical information system (GIS) and statistical data mining techniques. The major objective of this study is to identify the appropriate combination of tree for plantation purpose based on soil parameters. A total of 25 soil samples have been collected from in the Nanded Municipal Corporation. The result obtained from this study reveals that, correlation matrix, cluster and principle component analysis shows the significant soil parameters viz. pH, EC, magnesium, calcium and moisture. The spatial distribution maps of soil parameters provides the useful and prominent illustration tool in decision making process. For Green City/Green India programs spatial variation of soil parameters will be effective with GIS and statistical modelling approach and it may be helpful for green infrastructure development by using standard policies.


GIS Clustering Principle component analysis Soil interpolation Nanded 


  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large databases (VLDB’94), Santiago, Chile: 487–499Google Scholar
  2. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International conference on management of data,Washington, DC, pp 207–216Google Scholar
  3. CPCB (2002) Central pollution control board guideline.
  4. Dlamini WM (2016) Analysis of deforestation patterns and drivers in Swaziland using efficient Bayesian multivariate classifiers. Model Earth Syst Environ 2(4):173CrossRefGoogle Scholar
  5. Esmaeelnejad L, Siavashi F, Seyedmohammadi J, Shabanpour M (2016) The best mathematical models describing particle size distribution of soils. Model Earth Syst Environ 2(4):166CrossRefGoogle Scholar
  6. Fernández-Moya J, Alvarado etal (2014) Using multivariate analysis of soil fertility as a tool for forest fertilization planning. Nutr Cycl Agroecosyst 98(2):155–167CrossRefGoogle Scholar
  7. Gerstenberg T, Hofmann M (2016) Perception and preference of trees: a psychological contribution to tree species selection in urban areas. Urban Forest Urban Green 15:103–111CrossRefGoogle Scholar
  8. Jing L, Ng MK, Huang JZ (2007) An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans Knowl Data Eng 19(8):1026–1041Google Scholar
  9. Kaushik M, Mathur MB (2014) Comparative study of K-means and hierarchical clustering techniques. IJSHRE 2(6):93–98Google Scholar
  10. Khan A, Jana M, Bera S, Das A (2016) Subject choice in educational data sets by using principal component and procrustes analysis. Model Earth Syst Environ 2(4):209CrossRefGoogle Scholar
  11. Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci 275:1–12CrossRefGoogle Scholar
  12. Kulkarni G, Nilesh D, Parag B (2017) Effective use of GIS based spatial pattern technology for urban greenery space planning: a case study for Ganesh Nagar area of Nanded city. In: Proceedings of 2nd international conference on intelligent computing and applications, pp 123–132Google Scholar
  13. Miina J, Saksa T (2006) Predicting regeneration establishment in Norway spruce plantations using a multivariate multilevel model. New For 32(3):265–283CrossRefGoogle Scholar
  14. Mishra SP, Mishra D, Patnaik S (2015) An integrated robust semi-supervised framework for improving cluster reliability using ensemble method for heterogeneous datasets. Karbala Int J Modern Sci 1(4):200–211CrossRefGoogle Scholar
  15. Nosrati K (2015) Application of multivariate statistical analysis to incorporate physico-chemical surface water quality in low and high flow hydrology. Model Earth Syst Environ 1(3):19CrossRefGoogle Scholar
  16. Omran ESE (2016) Environmental modelling of heavy metals using pollution indices and multivariate techniques in the soils of Bahr El Baqar, Egypt. Model Earth Syst Environ 2(3):1–17CrossRefGoogle Scholar
  17. Sanquetta CR, Wojciechowski J, Dalla Corte AP, Rodrigues AL, Maas GCB (2013) On the use of data mining for estimating carbon storage in the trees. Carbon Balance Manag 8(1):6CrossRefGoogle Scholar
  18. Sitanggang IS, Yaakob R, Mustapha N, Ainuddin AN (2013) Classification model for hotspot occurrences using spatial decision tree algorithm. J Comput Sci 9(2):244–251Google Scholar
  19. Tang J, Zhou Z, Niu J, Wang Q (2014) An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things. J Netw Comput Appl 40:1–11CrossRefGoogle Scholar
  20. Tran L (2016) An interactive method to select a set of sustainable urban development indicators. Ecol Ind 61:418–427CrossRefGoogle Scholar
  21. Tsai CF, Wu HC, Tsai CW (2002) A new data clustering approach for data mining in large databases. In: Parallel architectures, algorithms and networks, 2002. I-SPAN’02. Proceedings. IEEE international symposium, pp 315–320Google Scholar
  22. Vázquez HJ, Juganaru-Mathieu M (2016) Exploring Urban Tree Site Planting Selection in Mexico City through Association Rules. In KDIR, pp 425–430Google Scholar
  23. Wenchuan Q, Dickman M, Sumin W (2001) Multivariate analysis of heavy metal and nutrient concentrations in sediments of Taihu Lake, China. Hydrobiologia 450(1–3):83–89Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Govind Eknath Kulkarni
    • 1
    Email author
  • Aniket Avinash Muley
    • 2
    Email author
  • Nilesh Kailasrao Deshmukh
    • 1
  • Parag Upendra Bhalchandra
    • 1
  1. 1.School of Computational SciencesSwami Ramanand Teerth Marathwada UniversityNandedIndia
  2. 2.School of Mathematical SciencesSwami Ramanand Teerth Marathwada UniversityNandedIndia

Personalised recommendations