تعداد نشریات | 161 |
تعداد شمارهها | 6,533 |
تعداد مقالات | 70,514 |
تعداد مشاهده مقاله | 124,131,125 |
تعداد دریافت فایل اصل مقاله | 97,237,361 |
بررسی گروهبندی خاک با استفاده از مدلهای خوشهبندی مرسوم و مدرن در بخشهایی از دشت قزوین | ||
تحقیقات آب و خاک ایران | ||
دوره 55، شماره 8، آبان 1403، صفحه 1273-1295 اصل مقاله (2.03 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2024.376397.669711 | ||
نویسندگان | ||
زهرا رسائی1؛ فریدون سرمدیان* 2؛ اعظم جعفری3 | ||
1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
2عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
3بخش علوم و مهندسی خاک، دانشکده کشاورزی-دانشگاه شهیدباهنر کرمان | ||
چکیده | ||
خاک به عنوان یکی از اجزای اصلی دستیابی به اهداف توسعه پایدار، نقش مهمی در مدیریت مسائل محیط زیستی دارد. بنابراین، تفکیک خاکهای با نیازهای مدیریتی مشابه ضروری میباشد. این امر باعث شده است که دانشمندان خاکشناسی از مدلهای طبقهبندی عددی برای گروهبندی خاکها بر اساس میزان شباهت آنها استفاده کنند. از میان مدلهای کمی ارائه شده در این زمینه، مطالعه حاضر دو مدل خوشهبندی مرسوم و مدرن را برای گروهبندی خاکهای اراضی قسمتهایی از دشت قزوین بکار برده است. بدین منظور، 297 خاکرخ مطالعه شده در منطقه بر اساس طیف گستردهای از ویژگیهای ریختشناسی، فیزیکی-شیمیایی و محیطی آنها با استفاده از مدلهای خوشهبندی یکطرفه و دوطرفه مورد گروهبندی قرار گرفتند. گروهبندیهای بدست آمده از این دو مدل بر اساس شاخصهای ارزیابی درونی و بیرونی (با در نظر گرفتن نقشه توزیع زیرگروههای خاک به عنوان نقشه مرجع واقعیت زمینی) مورد بررسی قرار گرفت. نتایج نشان داد، مدل خوشهبندی سلسله مراتبی با میزان کمتر شاخص دویس-بولدین (38/1DB:) و افزایش میزان شاخص رند تعدیلشده (49/0ARI: ) نسبت به مدل خوشهبندی دوطرفه کارایی بهتری دارد. با این حال، گروهبندیهای بدست آمده از مدل خوشهبندی دوطرفه به میزان قابل قبولی با تغییرات پستی و بلندی و تغییرات خاکها در منطقه تطابق دارند. این امر با میزان شاخص تفرق شانن بیشتر در مدل خوشهبندی دوطرفه (82/1) نسبت به مدل خوشهبندی سلسله مراتبی (62/1) تایید میشود. بطورکلی یافتههای این پژوهش، بر استفاده از مدل خوشهبندی دوطرفه به عنوان یک مدل داده کاوی مدرن در گروهبندی خاکها و یافتن الگوی تشابه مدیریتی آنها تاکید دارند. | ||
کلیدواژهها | ||
خوشهبندی دوطرفه؛ خوشهبندی سلسله مراتبی؛ نقشهبرداری رقومی خاک | ||
عنوان مقاله [English] | ||
Investigating soil grouping using conventional and modern clustering models in some parts of Qazvin plain | ||
نویسندگان [English] | ||
zahra rasaei1؛ Fereydoon Sarmadian2؛ Azam Jafari3 | ||
1Soil Science Department, Faculty of Agricultural, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
2soil science department< faculty of agricultural engineering and technology, university of Tehran | ||
3Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman | ||
چکیده [English] | ||
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. | ||
کلیدواژهها [English] | ||
Co-clustering, Digital soil mapping, Hierachical clustering | ||
مراجع | ||
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. | ||
آمار تعداد مشاهده مقاله: 119 تعداد دریافت فایل اصل مقاله: 81 |