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

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Rasaei Zahra; Sarmadian Fereydoon; Jafari Azam

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Journal Paper

Paper Information

Journal: Iranian Journal of Soil and Water Research Year:2024 | Volume:55 | Issue:8 Page(s): 1273-1295

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Information Journal Paper

Title

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

Author(s)

Rasaei Zahra | Sarmadian Fereydoon | Jafari Azam | Issue Writer Certificate

Keywords

Co-clustering

digital soil mapping

Hierachical clustering

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.

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