Use Of Self-organizing Maps Research Articles

Expansion of landscape characterisation methods within the Hydrogeological Landscape Framework: application in the Australian Capital Territory

ABSTRACT The Hydrogeological Landscape (HGL) Framework is a landscape-characterisation tool that is used to discern areas of similar physical, hydrogeological, hydrological, chemical and biological properties, referred to as HGL Units. The HGL Framework facilitates prioritisation of natural-resource management investment by identifying current and potential hazards in the landscape. Within prioritised regions, on-ground management actions are tailored for specific Management Areas within individual HGL Units. The HGL Unit boundaries are determined through expert interpretation of spatial and field based datasets, such as climate, landform, geology, regolith, soil, stream network, groundwater flow systems, water quality and vegetation assemblages. The resulting HGL Units are validated by an interdisciplinary team using field assessment and biophysical testing. The use of the HGL Framework for new applications creates opportunities for refinement of the existing methodology and products for end users. This paper uses an application in the Australian Capital Territory as a case study to illustrate two enhanced techniques for the landscape characterisation component of the HGL Framework: use of an unsupervised statistical learning algorithm, Self-Organising Maps (SOM), to further validate HGL Units; and landform modelling to assist in delineation of Management Areas. The combined use of SOM and landform modelling techniques provides statistical support to the existing expert and field-based techniques, ensuring greater rigour and confidence in determination of landscape patterns. This creates a more refined HGL Framework landscape-characterisation tool, facilitating more precise hazard assessment and strategic natural-resource management by end users.

Investigating event‐specific drought attribution using self‐organizing maps

AbstractPrevious studies evaluating anthropogenic influences on the meteorological drivers of drought have found mixed results owing to (1) the complex physical mechanisms which lead to the onset of drought, (2) differences in the characteristics and time scales of drought for different regions of the world, and (3) different approaches to the question of attribution. For a midlatitude, temperate climate like New Zealand, strongly modulated by oceanic influences, summer droughts last on the order of 3 months, and are less strongly linked to persistent temperature anomalies than continental climates. Here we demonstrate the utility of a novel approach for characterizing the meteorological conditions conducive to extreme drought over the North Island of New Zealand, using the January–March 2013 event as a case study. Specifically, we consider the use of self‐organizing map techniques in a multimember coupled climate model ensemble to capture changes in daily circulation, between two 41 year periods (1861–1901 and 1993–2033). Comparisons are made with seasonal pressure and precipitation indices. Our results demonstrate robust (>99% confidence) increases in the likelihood of observing circulation patterns like those of the 2013 drought in the recent‐climate simulations when compared with the early‐climate simulations. Best guess estimates of the fraction of attributable risk range from 0.2 to 0.4, depending on the metric used and threshold considered. Contributions to uncertainty in these attribution statements are discussed.

Use of self-organizing maps in the identification of different groups of reclamation sites in the amazon Forest-Brazil

Use of self-organizing maps in the identification of different groups of reclamation sites in the amazon Forest-Brazil

Simulating Cortical Maps for Attention Shift in Autism

Autism is a pervasive neuro-developmental disorder, primarily encompassing difficulties in the social, language, and communicative domains. Because autism is a spectrum disorder, it affects each individual differently and has varying degrees. There are three core aspects of impairment based upon the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), namely impairment in socialization, impairment in communication, and restricted repetitive activities or interests. This work describes the experiment aims at expressing autistic traits through the use of self-organizing map. Works related to simulating autism through self-organizing map is limited. This work compare and contrast the difference in attention index for normal learning and marred attention shift learning ability. It was found that the attention index of normal learning is 9 times better marred attention shift for both random and pre-fixed input data. In the marred attention shift context, neurons adapt more towards the mean of both sources combined under marred context while some neurons adapt towards mean of one source under normal context. The normal learning ability produces maps with neurons orienting towards mean values of combined stimuli source. Impairment in learning ability produces similar cortical maps compared to normal learning ability. The major difference is in the attention index.

Classification of groundwater chemistry in Shimabara, using self-organizing maps

Shimabara City in Nagasaki Prefecture, Japan, is located on a volcanic peninsula that has abundant groundwater. Almost all public water supplies use groundwater in this region. For this reason, understanding groundwater characteristics is a pre-requisite for proper water supply management. Thus, we investigated the groundwater chemistry characteristics in Shimabara by use of self-organizing maps (SOMs). The input to SOM was concentrations of eight major groundwater chemical components, namely Cl−, NO3−, SO42–, HCO3−, Na+, K+, Mg2+, and Ca2+ collected at 36 sampling locations. The locations constituted private and public water supply wells, springs, and a river sampled from April 2012 to May 2015. Results showed that depending on the chemistry, surface water and groundwater could be classified into five main clusters displaying unique patterns. Further, the five clusters could be divided into two major water types, namely, nitrate- and non-polluted water. According to Stiff and Piper trilinear diagrams, the nitrate-polluted water represented Ca-(SO4 + NO3) (calcium sulfate nitrate) type, while the non-polluted water was classified as Ca-HCO3 (calcium bicarbonate) type. This indicates that recharging rain water in the upstream areas is polluted by agricultural activities in the mid-slope areas of Shimabara.

Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data

Logbook data constitute a key element within the electronic recording and reporting system of the European Fisheries Control Technologies Framework and are used to record, report, process, store and send information about fishing operations, including landings and fishing gear. A relevant application of logbook data is to account for the heterogeneity of fishing practices (e.g., by gear or métier), which is a key aspect of the Common Fishery Policy. However, despite their importance, few published studies have explored the potential and pitfalls of logbook data, even in combination with other powerful data sources such as the Vessel Monitoring System (VMS). Here, a new approach to characterizing the composition of landings for the different types of gear based on the use of Self-Organizing Maps (SOMs − a particular type of Artificial Neural Network) is applied to the Italian fleet logbook dataset. The SOM is trained on the landings composition and the resulting patterns are interpreted using some measures obtained from the analysis of the corresponding VMS data. Namely, the mean sea bottom depth and the area of activity are obtained for each fishing trip. Moreover, the ability of the trained SOM to predict gear from landings is tested using a new dataset. The trained SOM classifies logbook records according to the ecological, taxonomical, and trophic characteristics of the species caught, and the depth of fishing activities plays an important role in diversifying the landings associated with certain widely used fishing gear such as the bottom otter trawl. The clustering of SOM units allows the identification of a set of 12 groups, which are strongly related to the types of gear used by the Italian fleet. Furthermore, the trained SOM shows a high ability to recognize gear from logbook data, thus confirming the robustness of the landings profiles detected.

Use of Self Organizing Map to Obtain ECG Data Templates for Its Compression and Reconstruction

An electrocardiogram (ECG) is a recording of electrical impulses generated by the electrical activity of the heart and is used as a diagnostic tool to analyze various heart diseases. For economical storage and fast transmission over low-bandwidth channels, ECG data need to be compressed. For the efficient compression of ECG signals, the topology preservation feature of self-organizing maps (SOM) is used. It is observed that a compression ratio up to 1:20 can be achieved with a very low-percentage root-mean-square difference, i.e. below 1.6, by creating templates of ECG patterns in the form of weight vectors of neurons. The templates obtained in this manner are then used to reconstruct the ECG signal. This analysis shows that the reconstructed signal is perfectly matched to the original signal.

Feature Extraction of Structures in Sea Water Using Self-Organizing Maps and Electromagnetic Waves

<pre><!--StartFragment-->The use of Self-Organizing Map (<span>SOM</span>) algorithm for feature extraction and dimensionality reduction applied to underwater object detection with Low Frequency Electromagnetic Waves is presented. Computer simulation is used to generate a direct model for the study region, and a Self Organizing Map Algorithm is used to fit the data and return a similar model, with smaller dimensionality and same characteristics. Results show that virtual sensors are created by the <span>SOM</span> algorithm with consistent predictions, filling the resolution gap of the input data. These results are useful for fastening decision making algorithms by reducing the number of inputs to a group of significant data.<!--EndFragment--></pre>

On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study.

In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component. ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution. Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects. This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts. A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

Construction and analysis of Hydrogeological Landscape units using Self-Organising Maps

The Hydrogeological Landscape (HGL) framework divides geographic space into regions with similar landscape characteristics. HGL regions or units are used to facilitate appropriate management actions tailored to individual HGL units for specific applications such as dryland salinity and climate-change hazard assessment. HGL units are typically constructed by integrating data including geology, regolith, soils, rainfall, vegetation and landscape morphology, and manually defining boundaries in a GIS environment. In this study, we automatically construct spatially contiguous regions from standard HGL data using Self-Organising Maps (SOM), an unsupervised statistical learning algorithm. We compare the resulting SOM-HGL units with manually interpreted HGL units in terms of their spatial distributions and attribute characteristics. Our results show that multiple SOM-HGL units successfully emulate the spatial distributions of individual HGL units. SOM-HGL units are shown to define subregions of larger HGL units, indicating subtle variations in attribute characteristics and representing landscape complexities not mapped during manual interpretation. We also show that SOM-HGL units with similar attributes can be selected using Boolean logic. Selected SOM-HGL units form regions that closely conform to multiple HGL units not necessarily connected in geographic space. These SOM-HGL units can be used to establish generalised land management strategies for areas with common physical characteristics. The use of SOM for the construction of HGL units reduces the subjectivity with which these units are defined and will be especially useful over large and/or inaccessible regions, where conducting field-based validation is either logistically or economically impractical. The methodology presented here has the potential to contribute significantly to land-management decision-support systems based on the HGL framework.

The application of self-organising maps to performance analysis data in rugby union

With the advent of professionalism in rugby union greater volumes of information are collected about player and team performance. A typical OptaTM, Sports CodeTM timeline, for a single rugby match, can have more than 2000 instances and labels of information. Unless there is a prior understanding of an opponent much time can be spent identifying irrelevant trends and information which may not fairly represent the performance of the match.Kohonen Self-organising Maps (SOMs) are a form of artificial neural network developed for clustering and visualising high-dimensional data by reducing the output to a low-dimensional output map. These visualisations may help the analyst quickly identify important relationships among the key performance indicators describing a match.In this paper we report on the application of SOMs to discrete data summarising matches in New Zealand’s ITM Cup rugby competition. The input variables were frequencies of common performance indicators. The SOM approach was used to narrow down the input variables to those that discriminate between successful and unsuccessful outcomes as well as map regions associated with various levels of success. Since map regions indicate game patterns or styles, further analysis showed that multiple game styles tended to lead to wins and multiple different styles tended to lead to losses. SOMs represent an important method for characterising game play in rugby union, we suggest continued use of SOMs will help make coaches and analysts more familiar with their interpretation and anticipate further streamlining of key performance indicator selection.

Use of self-organizing maps in modelling the distribution patterns of gammarids (Crustacea: Amphipoda)

Self-organizing maps (SOMs) are increasingly popular methods for processing high-dimensional ecological data; however, their potentials are not yet fully utilized. It was our objective to prove evidence on an unknown advantage of SOMs, which we aimed to test using data on the spatial distributional patterns of gammarids. Quantitative samples and a wide spectrum of environmental data were obtained from the catchment area of two of the largest side tributaries of the Tisza River. Distributional patterns and habitat preference of three Gammarus species were described by self-organizing map methods and regression tree analysis (CART) on spatial and temporal scale. Using SOMs helped us bring out distinctions in our data and enhance the differences, thus making them easier to recognize and also, with their help, we were able to model the relations of the species to habitat types that were non-existent among our samples. According to the analysis, Gammarus roeselii preferred low altitudes, high conductivity, fine substrate, deep actual mean depth and dense plant coverage; Gammarus fossarum preferred rocky stream beds, high altitudes, lower temperatures and little actual mean depth; while Gammarus balcanicus preferred coarse substrate, little or no plant coverage and low temperatures. SOMs improved the correlations that proved to be highly useful: besides their use in displaying complex data in a perspicuous way, they have other advantages in bringing out existing relationships in data that would otherwise be difficult to detect.

Assessment of the surface water quality ranking in Mudan River using multivariate statistical techniques

The present research deals with the important issue of assessing surface water quality ranking by combining the use of two advanced multivariate statistical techniques: Kohonen's self-organizing maps (SOM) and the Hasse diagram technique (HDT). The object of the study is the Mudan River of Mudanjiang city region, China. Samples were collected on a regular monthly basis in 2007–2011 from all sampling sites along the river, involving six major water quality parameters. The grouping of water parameters and the clustering of sampling events by the use of SOM has helped in their pre-processing for application of the HDT. The HDT orders clusters according to the pre-clustered water sampling events. The water quality was ranked against norms established by the Ministry of Environmental Protection of the People's Republic of China in order to assess in detail the water quality of the whole river system. The resulting map of the spatial and temporal changes in the water quality at each sampling site was specifically described by ArcGIS.

Atmospheric Environment and Quality of Life Information Extraction from Twitter with the Use of Self-Organizing Maps

The emergence of Web 2.0 technologies has changed dramatically not only the way users perceive the Internet and interact on it but also the way they influence a community and act in real life aspects. With the rapid rise in use and popularity of social media, people tend to share opinions and observations for almost any subject or event in their everyday life. Consequently, microblogging websites have become a rich data source for user-generated information. The leading opportunity is to take advantage of the wisdom of the crowd and to benefit from collective intelligence in any applicable domain. Towards this direction, we focus on the problem of mining and extracting knowledge from unstructured textual content, for the atmospheric environment domain and its effect to quality of life. As the main contribution, we propose a combined methodology of unsupervised learning methods for analyzing posts from Twitter and clustering textual data into concepts with semantically similar context. By applying Self-Organizing Maps and k-means clustering, we identify possible inter-relationships and patterns of words used in tweets that can form upper concepts of atmospheric and health related topics of discussion. We achieve to group together tweets, from more generic to more specific description levels of their content, according to the selected number of clusters. Strong clusters with significant semantic relatedness among their content are revealed, and hidden relations between concepts and their related semantics are acquired. The results highlight the potential use of social media text streams as a highly-valued supplement source of environmental information and situation awareness.

Self-organizing maps based on limit cycle attractors

Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent with the rhythmic oscillatory activity observed in the brain. Here we develop and study an alternative encoding scheme that instead uses sparsely-coded limit cycles to represent external input patterns/sequences. We establish conditions under which learned limit cycle representations arise reliably and dominate the dynamics in a SOM. These limit cycles tend to be relatively unique for different inputs, robust to perturbations, and fairly insensitive to timing. In spite of the continually changing activity in the map layer when a limit cycle representation is used, map formation continues to occur reliably. In a two-SOM architecture where each SOM represents a different sensory modality, we also show that after learning, limit cycles in one SOM can correctly evoke corresponding limit cycles in the other, and thus there is the potential for multi-SOM systems using limit cycles to work effectively as hetero-associative memories. While the results presented here are only first steps, they establish the viability of SOM models based on limit cycle activity patterns, and suggest that such models merit further study.

Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters

Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is based on curvelet subband Gaussian distribution parameter estimation and cascading these estimated values. The implemented method is compared against original data, polarimetric decomposition features and speckle noise reduced data with use of k-means, fuzzy c-means, spatial fuzzy c-means and self-organizing maps clustering methods. Experimental results show that the curvelet subband Gaussian distribution parameter estimation method with use of self-organizing maps has the best results among other feature extraction-clustering performances, with up to 94.94% overall clustering accuracies. The results also suggest that the implemented method is robust against speckle noise.

Novel approach to ecotoxicological risk assessment of sediments cores around the shipwreck by the use of self-organizing maps

Marine and coastal pollution plays an increasingly important role due to recent severe accidents which drew attention to the consequences of oil spills causing widespread devastation of marine ecosystems. All these problems cannot be solved without conducting environmental studies in the area of possible oil spill and performing chemometric evaluation of the data obtained looking for similar patterns among pollutants and optimize environmental monitoring during eventual spills and possible remediation actions – what is the aim of the work presented. Following the chemical and ecotoxicological studies self-organising maps technique has been applied as a competitive learning algorithm based on unsupervised learning process.Summarizing it can be stated that biotests enable assessing the impact of complex chemical mixtures on the organisms inhabiting particular ecosystems. Short and simple application of biotests cannot easily explain the observable toxicity without more complex chemometric evaluation of datasets obtained describing dependence between xenobiotics and toxicological results.

Discovery of Possible Gene Relationships through the Application of Self-Organizing Maps to DNA Microarray Databases

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

The Potential for Self-Organizing Maps to Identify Model Error Structures

Abstract An important aspect of numerical weather model improvement is the identification of deficient areas of the model, particularly deficiencies that are flow dependent or otherwise vary in time or space. Here the authors introduce the use of self-organizing maps (SOMs) and analysis increments from data assimilation to identify model deficiencies. Systematic increments reveal time- and space-dependent systematic errors, while SOMs provide a method for categorizing forecasts or increment patterns. The SOMs can be either used for direct analysis or used to produce composites of other fields. This study uses the forecasts and increments of 2-m temperature and dry column mass perturbation μ over a 4-week period to demonstrate the potential of this technique. Results demonstrate the potential of this technique for identifying spatially varying systematic model errors.

A Novel Approach for License Plate Slant Correction, Character Segmentation and Chinese Character Recognition

In this paper, the methods of correcting skew vehicle license plate and segmenting characters in plate are discussed first. An approach making use of self-organizing map (SOM) is introduced to find the tilt angle of plate which simultaneously educes seven important points with coordinates being elements of weight matrix. After necessary processing to corrected plate, a character segmentation algorithm based on the shortest distance classification is presented, which takes advantage of exactly seven points gained from SOM as class centers. In the next place, a hybrid algorithm cascading two steps of template matching is utilized to recognize Chinese characters segmented from the license plates, which is based on the connected region feature and standard deviation feature extracted from sample corresponding to each template. Experimental results show that the proposed method can be implemented efficiently.