Applications of Different Self‐Organizing Map Variants to Geographical Information Science Problems

Bação, Fernando; Lobo, Victor; Paìnho, Marco

doi:10.1002/9780470021699.ch2

Cited by 20 publications

(28 citation statements)

References 26 publications

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“…In SOM, the quantization error measures the resolution of SOM while the topographic error does the topology preservation of SOM. The quantization (0.335) and topographic (0.039) errors in this study were within the reasonable ranges of a previous application [41]. …”

Section: Parameter Estimationsupporting

confidence: 85%

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Kim

Baek

Ligaray

et al. 2015

Water

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Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.

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Section: Parameter Estimationsupporting

confidence: 85%

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Kim

Baek

Ligaray

et al. 2015

Water

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“…This tool is based on two major methods, the SOM [13] and the GeoSOM [17]. The SOM is a well-known algorithm that has proved to be of interest in spatial clustering.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper we propose the GeoSOM to tackle this limitation. The GeoSOM is an extension of SOM proposed in [17] for the specific difficulties posed by spatial data, effectively implementing the idea that "everything is related to everything else, but near things are more related than distant things". In fact, the GeoSOM uses geography as a restriction of nonspatial attributes clustering, thus all clustering is constrained by the geographic distance between cluster objects (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the GeoSOM uses geography as a restriction of nonspatial attributes clustering, thus all clustering is constrained by the geographic distance between cluster objects (i.e. objects in the same cluster are similar in attributes but have to be nearby geographically) This paper extends [17] by presenting a tool called GeoSOM suite. This tool implements the GeoSOM and the SOM algorithms, providing a friendly and ready to use data exploration tool.…”

Section: Introductionmentioning

confidence: 99%

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GeoSOM Suite: A Tool for Spatial Clustering

Henriques

Bação

Lobo

2009

Computational Science and Its Applications – ICCSA 2009

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Abstract. The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, which is based on Self-Organizing Maps. This paper describes this tool, and shows that it is adequate for exploring spatial data.

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“…As explained by Bação et al (2008), larger grids should be used for the purpose of exploring the data distribution and small grids are used when the user is interested in clustering, as in our case.…”

Section: Presentation Of the Toolsmentioning

confidence: 99%

A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage

Andrienko

Bak

Bremm

et al. 2010

Journal of Location Based Services

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Applications of Different Self‐Organizing Map Variants to Geographical Information Science Problems

Cited by 20 publications

References 26 publications

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

GeoSOM Suite: A Tool for Spatial Clustering

A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage

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