Self-organizing map artificial neural network application in multidimensional soil data analysis

Merdun, Hasan

doi:10.1007/s00521-010-0425-1

Cited by 25 publications

(22 citation statements)

References 18 publications

(19 reference statements)

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“…Similarly, Merdun (2011) and Rivera et al (2015) used the SOM for clustering the soil properties. The results of the research show that the SOM represents a powerful technique for Digital Soil Mapping (Merdun 2011;Rivera et al 2015). …”

Section: Resultsmentioning

confidence: 99%

“…The solutions were shaken for 2 h at 25°C, centrifuged, filtered, and Fe, Mn, Zn, and Cu concentrations were measured by an atomic absorption spectrophotometer (AAS) (PG 990, PG Instruments Ltd., Leicester, UK). Organic carbon of the soils as an index of organic N was measured by chromic acid oxidation (Merdun 2011). Matlab (Version 8.5) software was used to classify the features (available N, K, P, Fe, Mn, Zn and Cu) with the SOM algorithm ( Figure 2).…”

Section: Methodsmentioning

confidence: 99%

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Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Mokarram¹,

Najafi‐Ghiri²,

Zarei³

2018

Soil & Water Res.

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Mokarram M., Najafi-Ghiri M., Zarei A.R. (2018): Using self-organizing maps for determination of soil fertility (case study: Shiraz plain). Soil & Water Res., 13: 11−17.Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Mokarram¹,

Najafi‐Ghiri²,

Zarei³

2018

Soil & Water Res.

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“…They provide a useful tool for multivariate modelling of systems with non-linear correlation among variables. Two main applications of ANN may be identified: function approximation by retropropagation algorithms, and grouping or classification of input vectors (Merdun, 2011).…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Neural Networks (SOM) Applied to INAA Data of Chemical Elements in Archaeological Ceramics from Central Amazon

Hazenfratz

Munita

Neves

2017

STAR: Science & Technology of Archaeological Research

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Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi-or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data. ARTICLE HISTORY

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“…15 000 samples, 14 parameters and 23 sampling locations), combining chemical indicators such as pH, dissolved oxygen, biological oxygen demand, chloride and nitrate, among others. Merdun () explored a soil database (19 variables) to analyse relationships among soil, chemical and hydraulic soil properties. Our approach is similar to Merdun () and Astel et al .…”

Section: Introductionmentioning

confidence: 99%

“…Merdun () explored a soil database (19 variables) to analyse relationships among soil, chemical and hydraulic soil properties. Our approach is similar to Merdun () and Astel et al . () on the methodological aspects, but differs in the specific methods used to cluster the maps and the assessment of the quality of the clustering process.…”

Section: Introductionmentioning

confidence: 99%

Exploring soil databases: a self‐organizing map approach

Rivera

Sandoval

Godoy

2015

Soil Use and Management

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A soil quality database (SQDB) is a collection of soil samples described by a given set of parameters, allowing farmers, scientists and other stakeholders to make informed decisions about practices, processes and policies for soil use and management. If each parameter is considered as a dimension of the space spanned by the SQDB, extracting information becomes a difficult task when the number of parameters is >3. A widely used approach to explore multidimensional data sets is the self‐organizing map (SOM) method, which is suitable for clustering, visualization and extraction of information from multidimensional data. We applied the SOM method as an exploratory technique to an unlabelled SQDB to extract knowledge – data patterns and data associations – from the data set (the time and location of each sample were unknown). The SQDB used in this study is a set of 1240 unlabelled samples within the Central Valley of Chile, covering ca 7500 km2. The predominant soils are Andisols with a large organic matter content (7–12%), small bulk densities (0.6–1.0 g/cm3) and large water‐holding capacity. We identified three patterns: (i) isolated region within the map with close neurons (smooth transitions), (ii) two or more regions with predominantly large or small values and (iii) homogeneous map with small values with an isolated region of large values. These patterns show that the data set represented more than two groups that were not necessarily related. For pH, no important associations with other investigated parameters were observed. Previous studies carried out by the local agricultural research station showed that pH values below 5.5 constrain nutrient uptake. Thus, locations presenting pH<5,5 should be subject to seasonal monitoring to assess management practices that mitigate soil acidity. The component plane for organic matter indicates that ca. 50% of the soil samples had contents <8% related to soil series characteristics and management practices. As the k‐means is initialized by random partitions, the two‐step approach (clustering the map representing the input data) is less sensitive to variations in the input data (subsamples) than is the direct application of k‐means to the input data, but it also reduces the computational cost. The ability of SOMs to visualize multidimensional data sets helps gain an understanding of the data in the exploratory phase, such as the association and integration of physical, chemical and biological parameters.

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Self-organizing map artificial neural network application in multidimensional soil data analysis

Cited by 25 publications

References 18 publications

Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Neural Networks (SOM) Applied to INAA Data of Chemical Elements in Archaeological Ceramics from Central Amazon

Exploring soil databases: a self‐organizing map approach

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