Classification of Soil Groups Using Weights-of-Evidence-Method and RBFLN-Neural Nets

Tissari, Soile; Nykänen, Vesa; Lerssi, J.; Kolehmainen, Mikko

doi:10.1007/s11053-007-9040-y

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…At this point, we would like to bring up that, commonly, soil maps for hydrological modeling set-up are based on the taxonomic approach. In general, those maps were developed using a traditional interpretation of aerial photos, available geology maps, soil samples from field work and performing of laboratory analytics [23]. The taxonomic soil units' maps sometimes include additional information about specific soil properties, although this is not the case.…”

Section: Soil Map Properties Sources For Hydrological Modellingmentioning

confidence: 99%

Self-organizing map of soil properties in the context of hydrological modeling

Rivas-Tabares

Miguel

Willaarts

et al. 2020

Applied Mathematical Modelling

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Section: Soil Map Properties Sources For Hydrological Modellingmentioning

confidence: 99%

Self-organizing map of soil properties in the context of hydrological modeling

Rivas-Tabares

Miguel

Willaarts

et al. 2020

Applied Mathematical Modelling

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“…Riparian zones can also experience seasonal hydrologic uctuations resulting from changes in connectivity with the upland aquifer (Vidon & Hill, 2004) and variability in water inputs due to seasonal precipitation patterns. (Tissari et al, 2007). Additionally, the SOM approach has been utilized to address questions concerning water resources and hydrology, such as rainfall-runoff relationships (Lin & Chen, 2006), precipitation dynamics (Kalteh et al, 2008), and links between physical soil properties and hydrologic soil processes (Merdun, 2011).…”

Section: Introductionmentioning

confidence: 99%

Complex Drivers of Riparian Soil Oxygen Variability Revealed Using Self-Organizing Maps

Lancellotti

Underwood

Perdrial

et al. 2021

Preprint

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Oxygen (O2) is a key regulator of soil reduction-oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O2 variability have prompted the use of soil moisture as a proxy for soil O2, based on the low solubility of O2 in water. Due to seasonal shifts in soil O2 depletion mechanisms, the use of soil moisture alone as a proxy measurement could result in inaccurate O2 estimations. For example, soil O2 may remain high during cool months when soil respiration rates are low. We analyzed high-frequency sensor data (e.g., soil moisture, temperature, CO2, O2) with a machine learning technique, the Self-Organizing Map, to pinpoint suites of soil conditions that are associated with contrasting O2 regimes. At two low-lying riparian sites in contrasting land use and topographic settings of northern Vermont, we found that soil O2 levels varied seasonally, and with soil moisture. For example, forty-seven percent of low O2 levels were associated with cool and wet soil conditions, whereas 32% were associated with warm and dry conditions. Contrastingly, the majority (62%) of high O2 conditions occurred under warm and dry conditions. High soil moisture levels did not always lead to low O2, however, as 38% of high O2 values occurred under cool and wet conditions. Our results highlight challenges associated with predicting soil O2 solely based on soil moisture, as variable combinations of soil and site-specific hydrologic conditions can complicate the relationship between soil water content and O2. This indicates that process-based ecosystem and denitrification models that rely solely on soil moisture to estimate O2 availability will, in some cases, need to incorporate other site and climate-specific drivers to accurately predict soil O2.

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“…The SOM method has been successfully applied in classification of sediment samples (Alvarez-Guerra et al, 2008, 40 samples, 13 parameters), sediment chemistry (Astel et al, 2007), soil classification using data from near-infrared spectroscopy (Fidencio et al, 2001), soil group classification (Tissari et al, 2007), sole-carbon source utilization profiles (Leflaive et al, 2005), soil biological and chemical quality (Mele & Crowley, 2008;Dhar & Cherkassky, 2011), soil bacterial community comparisons (Wu et al, 2008), water quality and hydrological records (Kalteh et al, 2008) and ecological sciences (Chon, 2011). Astel et al (2007) pointed out that the SOM method has a stronger 'resolving power' for classification than PCA in analysing large data sets (ca.…”

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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Classification of Soil Groups Using Weights-of-Evidence-Method and RBFLN-Neural Nets

Cited by 13 publications

References 5 publications

Self-organizing map of soil properties in the context of hydrological modeling

Self-organizing map of soil properties in the context of hydrological modeling

Complex Drivers of Riparian Soil Oxygen Variability Revealed Using Self-Organizing Maps

Exploring soil databases: a self‐organizing map approach

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