Investigating soil grouping using conventional and modern clustering models in some parts of Qazvin plain

Document Type : Research Paper

Authors

1 Soil Science Department, Faculty of Agricultural, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 soil science department< faculty of agricultural engineering and technology, university of Tehran

3 Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman

Abstract

Soil is a crucial component in achieving sustainable development goals due to its significant role in addressing environmental challenges. It is essential to differentiate soils that have similar management requirements. This necessity has prompted soil scientists to employ numerical classification models to categorize soils based on their similarities. In this study, we utilized two types of clustering models, traditional and modern, to classify soils from certain areas of the Qazvin Plain. Using one-way and two-way clustering models, we grouped 297 soils from the region based on a comprehensive set of their morphological, physicochemical, and environmental attributes. The classifications derived from these two models were assessed using internal and external evaluation indicators, with the distribution map of soil subgroups serving as a ground truth reference map. The results indicated that the hierarchical clustering model, with a lower Davis-Bouldin index (DB: 1.38) and a higher adjusted Rand index (ARI: 0.49), outperformed the biclustering model. However, the classifications from the bidirectional clustering model corresponded reasonably well with the topographical and soil changes in the region, as evidenced by the higher Shannon’s difference index in the bidirectional clustering model (1.82) compared to the hierarchical clustering model (1.62). Overall, the study’s findings underscore the utility of the co-clustering model as a contemporary data mining technique for soil classification and identification of soil management similarity patterns.

Keywords

Main Subjects

EXTENDED ABSTRACT

Introduction

The classification of soils is crucial for their proper management and identification. Soil scientists have recognized the importance of numerical classification models for soil grouping. Creating continuous maps using digital mapping models allows for a better understanding of soil class distribution, aiding in improved soil management. Traditional clustering methods group soils based on their distinct properties, while two-way clustering methods group soils within subsets of similar characteristics. This study aims to compare the effectiveness of one-way and two-way clustering models in soil grouping and in identifying relationships between different soils.

Materials and Methods

This study utilized a dataset of 297 soil samples from some parts of the Qazvin plain. A broad spectrum of morphological, physicochemical, and environmental variables was used for soil grouping. The hierarchical clustering method was employed for one-way soil grouping, and the two-way clustering method was used for co-clustering of them. The Davis-Bouldin (DB) index was used to evaluate the groupings obtained from these models based on the degree of soil separation and intra-group variance. The Adjusted Rand Index (ARI) was used for external evaluation of groupings, with the distribution map of soil subgroups serving as a ground truth reference. Shannon’s entropy index was used to assess the efficiency of these models in representing soil variability in the study area.

Results and Discussion

The study found that both models were successful in differentiating the region's soils based on topographical and physiographic unit changes. However, the two-way clustering model demonstrated a slightly different pattern in soil separation, particularly in the central and southern parts of the study area. A numerical comparison of the results showed that the one-way clustering model provided better soil separation and less variance (DB: 1.38), and was more congruent with the distribution map of soil subgroups in the region (ARI: 0.49). The two-way clustering model effectively represented the pattern of soil changes in the study area, as evidenced by a higher Shannon index (1.82) compared to the hierarchical clustering model (1.62).

Conclusion

Although numerical comparative evaluations of the groupings reveal the superior efficiency of the hierarchical clustering model in separating soil groups, the two-way clustering model successfully grouped the region's soils according to their changes and the region's physiographic changes. This model also effectively represented soil changes in the region, as indicated by a higher Shannon entropy index. The study's findings affirm the efficacy of the two-way clustering model as a modern data mining technique in identifying similar soils and, consequently, in their grouping and modeling in digital soil mapping studies. The use of this model is recommended for examining soils in different parts of the country

Author Contributions

Conceptualization, Z.R., F.S. and A.J.; methodology, Z.R., and A.J.; software, Z.R.; validation, Z.R., and A.J.; formal analysis, Z.R.; investigation, Z.R., F.S. and A.J.; resources, Z.R. and F.S.; data curation, F.S.; writing—original draft preparation, Z.R.; writing—review and editing, Z.R., F.S. and A.J.; visualization, Z.R., F.S. and A.J.; supervision, F.S.; project administration, F.S.; funding acquisition, Z.R. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Not applicable.

Acknowledgements

This work is based on research funded by the Iran National Science Foundation (INSF) under project No. 4024343. The first author would like to express her appreciation to the INSF for its financial support of this project, and to the University of Tehran for providing the conditions necessary for the completion of this project.

Ethical considerations

The study was approved by the Ethics Committee of the University of ABCD (Ethical code: IR.UT.RES.2024.500). The authors avoided data fabrication, falsification, plagiarism, and misconduct. 

References

Adams, M. B., Turner, R. S., & Schmoyer, D. D. (1992). Evaluation of direct delayed response project soil sampling classes: Northeastern United States. Soil Science Society of America Journal, 56, 177–187.
Adhikari, K., Minasny, B., Greve, M. B., & Greve, M. H. (2014). Constructing a soil class map of Denmark based on the FAO legend using digital techniques. Geoderma, 214-215, 101-113.
Aouabed, H., Elloumi, M., & Santamaría, R. (2021). An evaluation study of biclusters visualization techniques of gene expression data. Journal of Integrative Bioinformatics, 18(4), 20210019.
Arnold, R. (2006). Soil Survey and Soil Classification. In S. Grunwald (Ed.), Environmental Soil-Landscape Modeling: Geographic Information Technologies and Pedometrics (pp. 37–60). CRC Press.
Aubert, J., Schbath, S., & Robin, S. (2021). Model‐based biclustering for overdispersed count data with application in microbial ecology. Methods in Ecology and Evolution, 12(6), 1050-1061.
Beaudette, D. E., Roudier, P., & O’Geen, A. T. (2023). Algorithms for quantitative pedology: A toolkit for soil scientists. Computers and Geosciences, 52, 258–268.
Bekele, A., Beyene, S., Yimer, F., & Kiflu, A. (2024). Numerical classification of termite-mediated soils along toposequences and rangeland use influenced soil properties in southeast Ethiopia. Heliyon, 10(1), e23726.
Bouma, J., Montanarella, L., & Evanylo, G. (2019). The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals. Soil Use and Management, 35(4), 538-546.
Carré, F., & Jacobson, M. (2009). Numerical classification of soil profile data using distance metrics. Geoderma, 148(3), 336–345.
Cebeci, Z., & Yildiz, F. (2015). Comparison of K-means and Fuzzy C-means algorithms on different cluster structures. Journal of Agricultural Informatics, 6(3), 13-23.
Chakraborty, A., & Vardeman, S. B. (2022). Some Bayesian biclustering methods: Modeling and inference. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 409-538.
Cheng, Y., & Church, G.M. (2000). Biclustering of Expression Data. Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, 93–103.
Chiquet, J., Rigaill, G., Sundqvist, M., Dervieux, V., & Bersani, F. (2023). Efficient Computations of Standard Clustering Comparison Measures, R Package ‘aricode’. CRAN.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991-2007.
Dane, J. H., & Topp, C. G. (Eds.). (2020). Methods of soil analysis, Part 4: Physical methods (Vol. 20). John Wiley & Sons.
Davies, D. L., & Bouldin, D. W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224-227.
de França, F. O., & Coelho, A. L. V. (2015). A biclustering approach for classification with mislabeled data. Expert Systems with Applications, 42(12), 5065–5075.
Deressa, A., Yli-halla, M., Mohamed, M., & Wogi, L. (2018). Soil classification of humid Western Ethiopia: a transect study along a toposequence in Didessa watershed. Catena, 163, 184-195.
Dunkl, I., & Ließ, M. (2022). On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization. SOIL, 8, 541-558.
Edokpayi, A. A., Agho, C. A., Adeh, S. A., & Okpamen, S. U. (2017). Comparison of the Different Hierarchical Clustering Techniques for the Classification of Soils under Oil Palm in Nigeria. International Journal of Basic Science and Technology, 3(1), 37-46.
Egbueri, J. C. (2023). Soil erosion and landslide susceptibility insights based on hierarchical clustering and multilayer perceptron networks: a Nigerian case study. Volume 20, pages 10763–10786.
Esfandiarpour Borujeni, I., & Bagheri Bodaghabadi, M. (2016). Analysis of uncertainty of a soil map using taxonomic adjacency and pedodiversity indices. Journal of Soil Management and Sustainable Production, 5(4), 147-160. (In Persian with English abstract).
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley.
Fernando, E. S., Balatibat, J. B., Peras, J. R., & Jumawid, R. J. J. (1998). Resource inventory and assessment of biodiversity in Subic Bay Metropolitan Authority. Terminal Report. Research Project funded by DOST-PCARRD, SBMA and UPLB.
Gad, M.M.S., Mohamed M.H.A., Mohamed, M.R. (2021). Soil salinity mapping using remote sensing and GIS. Geomatica, 75(4), 295-309.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
Handl, J., Knowles, J., & Kell, D. B. (2005). Computational cluster validation in post-genomic data analysis. Bioinformatics, 21(15), 3201-3212.
Hartigan, J. A. (1972). Direct Clustering of a Data Matrix. Journal of the American Statistical Association, 6(337), 123–129.
Heil, J., Häring, V., Marschner, B., & Stumpe, B. (2019). Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils. Geoderma, 337, 11-21.
Hu, C., Wright, A. L., & Lian, G. (2019). Estimating the Spatial Distribution of Soil Properties Using Environmental Variables at a Catchment Scale in the Loess Hilly Area, China. International Journal of Environmental Research and Public Health, 16(3), 491.
Hughes, P. A., McBratney, A. B., Minasny, B., & Campbell, S. (2014). End members, end points and extragrades in numerical soil classification. Geoderma, 226-227, 365-375.
Ibanez, J. J., De Alba, S., Bermudes, F. F., & Garcia-Alvarez, A. (1995). Pedodiversity: concepts and measurements. Catena, 24, 215–232.
Index DataBase. (2021). A Database for Remote Sensing Indices. Retrieved from https://www.indexdatabase.de/.
Jafari, A., Ayoubi, S., Khademi, H., Finke, P. A., & Toomanian, N. (2013). Selection of a taxonomic level for soil mapping using diversity and map purity indices: a case study from an Iranian arid region. Geomorphology, 201, 86-97.
Jafari, A., Finke, P. A., Wauw, J. V., Ayoubi, S., & Khademi, H. (2012). Spatial prediction of USDA great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. European Journal of Soil Science, 63, 284-298.
Jhariya, M. K., & Singh, L. (2021). Herbaceous diversity and biomass under different fire regimes in a seasonally dry forest ecosystem. Environment, Development and Sustainability, 23(5), 6800-6818.
Kaiser, S. (2011). Biclustering: Methods, Software and Application.
Kaiser, S., Santamaria, R., Khamiakova, T., Sill, M., Theron, R., Quintales, L., Leisch, F., De Troyer, E., & Leon, S. (2023). biclust: BiCluster Algorithms.
Kassambara, A. (2017). Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning. STHDA.
Ketchen Jr., D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic Management Journal, 17(6), 441-458.
Khamoshi, S. E., Sarmadian, F., & Keshavarzi, A. (2019). Digital soil mapping Using Random Forests and Land Suitability Evaluation for Abyek Region, Qazvin Province. Journal of Range and Watershed Management, 71(4), 885-899. (In Persian with English abstract).
Khamoshi, S. E., Sarmadian, F., & Omid, M. (2023). Predicting and Mapping of Soil Organic Carbon Stock Using Machine Learning Algorithm. Iranian Journal of Soil and Water Research, 53(11), 2671-2681. (In Persian with English abstract).
Láng, V., Fuchs, M., Waltner, I., & Michéli, E. (2013). Soil taxonomic distance, a tool for correlation: As exemplified by the Hungarian Brown Forest Soils and related WRB Reference Soil Groups. Geoderma, 192, 269-276.
Lee, D. B., Kim, Y. N., Sonn, Y. K., & Kim, K. H. (2023). Comparison of Soil Taxonomy (2022) and WRB (2022) Systems for classifying Paddy Soils with different drainage grades in South Korea. Land, 12(6), 1204.
Li et al. (2020). Biclustering with missing data. Information Sciences, 510, 304-316.
Liberti, L., Lavor, C., Maculan, N., & Mucherino, A. (2014). Euclidean distance geometry and applications. SIAM review, 56(1), 3-69.
Liu, H., Yang, J., Ye, M., Tang, Z., Dong, J., & Xing, T. (2021). Using one-way clustering and co-clustering methods to reveal spatio-temporal patterns and controlling factors of groundwater geochemistry. Journal of Hydrology, 603, 127085.
Malone, B. P., McBratney, A. B., Minasny, B., & Laslett, G. M. (2009). Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154(1), 138–152.
McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52.
Minasny, B., McBratney, A. B., & Hartemink, A. E. (2010). Global pedodiversity, taxonomic distance, and the World Reference Base. Geoderma, 155, 132–139.
Momtazi Burojeni, M., & Sarmadian, F. (2023). Spatial prediction of soil classes using C5.0 boosted decision tree model in Abyek Area. Iranian Journal of Soil and Water Research, 75(4), 553-572. (In Persian with English abstract).
Mousavi, S. R. A., Sarmadian, F., & Rahmani, A. (In Persian with English abstract). Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain. Journal of Water and Soil, 50(10), 2525-2538. (In Persian with English abstract).
Mousavi, S. R. A., Sarmadian, F., Omid, M., & Bogaert, P. (2021). Modeling the Vertical Soil Calcium Carbonate Equivalent Variation by Machine Learning Algorithms in Qazvin Plain. Journal of Water and Soil, 35(5), 719-734. (In Persian with English abstract).
Mousavi, S. R. A., Sarmadian, F., Omid, M., & Bogaert, P. (2021). Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 52(7), 1915-1929. (In Persian with English abstract).
Mousavi, S. R. A., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Application of Machine Learning Models in Spatial Estimation of Soil Phosphorus and Potassium in Some Parts of Abyek Plain. Iranian Journal of Soil Science, 35(4), 397-411. (In Persian with English abstract).
Mousavi, S. R., Sarmadian, F., Angelini, M. E., Bogaert, P., & Omid, M. (2023). Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions. Catena, 232, 107392.
Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement, 201, 111706.
Nachtergaele, F., van Velthuizen, H., Verelst, L., Wiberg, D., Henry, M., Chiozza, F., … & Tramberend, S. (2023). Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations.
Naimi, S., Ayoubi, S., Zeraatpisheh, M., & Dematte, J. A. M. (2021). Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach. Remote Sensing, 13(23), 4825.
Neyestani, M., Sarmadian, F., Jafari, A., Keshavarzi, A., & Sharififar, A. (2021). Digital mapping of soil classes using spatial extrapolation with imbalanced data. Geoderma Regional, 26, e00422.
Noronha, M. D., Henriques, R., Madeira, S. C., & Zárate, L. E. (2022). Impact of metrics on biclustering solution and quality: A review. Pattern Recognition, 127, 108612.
Orzechowski, P., Boryczko, K., & Moore, J. H. (2019). Scalable biclustering—the future of big data exploration? GigaScience, 8(7), giz078.
Pham, H., Reisner, J., Swift, A., Olafsson, S., & Vardeman, S. (2022). Crop phenotype prediction using biclustering to explain genotype-by-environment interactions. Frontiers in Plant Science, 13, 975976.
Pontes, B., Giráldez, R., & Aguilar-Ruiz, J. S. (2015). Biclustering on expression data: A review. Journal of Biomedical Informatics, 57, 163–180.
R Development Core Team. (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna Austria.
Rahmani, A., Sarmadian, F., & Arefi, H. (2023). Digital modeling and prediction of soil subgroup classes using deep learning approach in a part of arid and semi-arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 53(11), 2477-2499. (In Persian with English abstract).
Rasaei, Z., Mohammadi, J., & Jafari, A. (2021). Comparison of grouping and the quality of legacy soil map boundaries with numerical data mining models: a case study of some regions of Chaharmahal-va-Bakhtiari province. Applied Soil Research, 8(4), 28-43. (In Persian with English abstract).
Rasaei, Z., Rossiter, D.G., & Farshad, A. (2020). Rescue and renewal of legacy soil resource inventories in Iran as an input to digital soil mapping. Geoderma regional, 21, e00262.
Rezaie, G., Sarmadian, F., Mohammadi Torkashvand, A., Seyedmohammadi, J., & Marashi Aliabadi, M. (2023). Digital mapping of surface and subsurface soil organic carbon and soil salinity variation in a part of Qazvin plain (Case study: Abyek and Nazarabad regions). Journal of Water and Soil, 37(2), 315-331. (In Persian with English abstract).
Romanski, P., Kotthoff, L., & Schratz, P. (2023). FSelector R Package. CRAN.
Saldana, A., & Ibanez, J.J. (2004). Pedodiversity analysis at large scales: an example of three fluvial terrains of the Henares River (central Spain). Geoderma, 62, 123–1384.
Schoeneberger, P.J., Wysocki, D.A., Benham, E.C., & Soil Survey Staff. (2021). Field book for describing and sampling soils, Version 3.0. Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE.
Seaton, F. M., Barrett, G., Burden, A., Creer, S., Fitos, E., Garbutt, A., Griffiths, R. I., Henrys, P., Jones, D. L., Keenan, P., Keith, A., Lebron, I., Maskell, L., Pereira, M. G., Reinsch, S., Smart, S. M., Williams, B., Emmett, B. A., & Robinson, D. A. (2021). Soil health cluster analysis based on national monitoring of soil indicators. European Journal of Soil Science, 72(6), 2414-2429.
Selmy, S., Abd El-Aziz, S., El-Desoky, A., & El-Sayed, M. (2022). Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches. Journal of the Saudi Society of Agricultural Sciences, 21(6), 383-396.
Shelia, V., & Hoogenboom, G. (2020). A new approach to clustering soil profile data using the modified distance matrix.
Sistani, N., Moein-Aldini, M., Ali Taleshi, M. S., Khorasani, N. A., Hamidian, A. H., & Azimi Yancheshmeh, R. (2018). Source identification of heavy metal pollution nearby Kerman steel industries. Journal of Natural Environment, 70(3), 627-641. (In Persian with English abstract).
Soil Survey Staff. (2022). Keys to Soil Taxonomy, 13th ed. USDA-Natural Resources Conservation Service.
Sonn, Y., Seo, B., Go, W., Jeon, S., Hyun, B., & Yun, S. (2019). Consideration of suffix symbol on soil taxonomy and world reference base for soil resources classification. Korean Journal of Soil Science and Fertilizer, 52(4), 345-351.
Soropa, G. A., Mbisva, O. M., Nyamangara, J., Nyakatawa, E. Z., Nyapwere, N., & Lark, R. M. (2021). Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe. SN Applied Sciences, 3(396).
Sparks, D. L., Page, A. L., Helmke, P. A., & Loeppert, R. H. (Eds.) (2020). Methods of soil analysis, part 3: Chemical methods (Vol. 14). John Wiley & Sons.
Sunori, S. K., Pant, J., Pant, H., Manu, M., & Juneja, P. (2022, October). Design of K-Means Clustering & SVM based Models for Soil Fertility Classification. In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 862-866). IEEE.
Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., & Malone, B. P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28.
Taghizadeh-Mehrjardi, R., Nabiollahi, K., Minasny, B., & Triantafilis, J. (2015). Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma, 253-254, 67-77.
Tchorbadjieff, A., Kostev, T., Stoyanova, V., & Tcherkezova, E. (2019). K-means clustering of a soil sampling scheme with data on the morphography of the Ogosta Valley Northwestern Bulgaria. European Journal of Geography, 10(2).
Van Wambeke, A. R. (2000). The Newhall Simulation Model for estimating soil moisture & temperature regimes. Conservation Service: Department of Crop and Soil Sciences Cornell University, Ithaca, NY USA.
Walesiak, M. (2023). clusterSim: Searching for Optimal Clustering Procedure for a Data Set. R package version 0.50-23.
Wang, N., Chen, S., Huang, J., Frappart, F., Taghizadeh, R., Zhang, X., … & Shi, Z. (2024). Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing. Journal of Remote Sensing, 4, 0130.
Weatherill, G., & Burton, P. W. (2009). Delineation of shallow seismic source zones using K-means cluster analysis, with application to the Aegean region. Geophysical Journal International, 176(2), 565–588.
Wilding, L. P. (1985). Spatial variability: its documentation, accommodation and implication to soil survey. In D. R. Nielsen & J. Bouma (Eds.), Soil Spatial Variability (pp. 166-189). Pudoc.
Xie, J., Guo, A.-Y., Fennell, A., Ma, Q., & Zhao, J. (2019). It is time to apply biclustering: A comprehensive review of biclustering applications in biological and biomedical data. Briefings in Bioinformatics, 20(4), 1450–1465.
Xu, H., Croot, P., & Zhang, C. (2021). Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis. Environment International, 151, 106456.
Zhang, X., & Huang, B. (2019). Prediction of soil salinity with soil-reflected spectra: a comparison of two regression methods. Scientific Reports, 9, 5067.
Zhao, W., Ma, J., Liu, Q., Song, J., Tysklind, M., Liu, C., Wang, D., Qu, Y., Wu, Y., & Wu, F. (2023). Comparison and application of SOFM, fuzzy c-means and k-means clustering algorithms for natural soil environment regionalization in China. Environmental Research, 216(Pt 2), 114519.
Zirnea, S., Lazar, I., Foudjo, B.U.S., Vasilache, T., & Lazar, G. (2013). Cluster Analysis Based of Geochemical Properties of Phosphogypsum Dump Located Near Bacau City in Romania. APCBEE Procedia, 5, 317–322.
Zolfaghari, F., Khosravi, H., Shahriyari, A., Jabbari, M., & Abolhasani, A. (2019). Hierarchical cluster analysis to identify the homogeneous desertification management units. PLoS One, 14(12), e0226355.
Iranian Journal of Soil and Water Research
Volume 55, Issue 8
October 2024
Pages 1273-1295

Files

Share

How to cite

Statistics