Self-organizing Map Analysis Research Articles

Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply

The 2016 Kumamoto earthquake had a significant impact on groundwater levels and quality. In some areas, the groundwater level increased significantly due to the release of groundwater from upstream mountainous regions. Conversely, the groundwater level in other areas greatly decreased due to the creation of new fracture networks by the earthquake. There were also significant changes in certain groundwater quality variables. In this study, we used clustering based SOM (self-organizing maps) analysis to improve the understanding of earthquake effects on groundwater quality. We were especially interested in effects on groundwater used for drinking purposes and in nitrate concentration. For this purpose, we studied groundwater nitrate (NO3− + NO2−–N) concentrations for the period 2012–2017. Nitrate concentration changes were classified into seven typical SOM clusters. The clusters were distributed in three representative geographical regions: a high concentration region (>4 mg/L), a low concentration region (<1.6 mg/L) with minimal anthropogenic loading area, and an intermediate concentration region (2–4 mg/L). Depending on these regions, the nitrate concentration changes just before and after the earthquake had both increasing and decreasing trends between 2015–2017. This points to complex physiographical relationships for release of stored upstream groundwater, promotion of infiltration of shallow soil water/groundwater, and nitrate concentration as affected by earthquakes. We present an analysis of these complex relationships and a discussion of causes of nitrate concentration changes due to earthquakes.

Stability analysis of a neural field self-organizing map

We provide theoretical conditions guaranteeing that a self-organizing map efficiently develops representations of the input space. The study relies on a neural field model of spatiotemporal activity in area 3b of the primary somatosensory cortex. We rely on Lyapunov’s theory for neural fields to derive theoretical conditions for stability. We verify the theoretical conditions by numerical experiments. The analysis highlights the key role played by the balance between excitation and inhibition of lateral synaptic coupling and the strength of synaptic gains in the formation and maintenance of self-organizing maps.

Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?

Infinite factors can influence the spread of COVID-19. Evaluating factors related to the spread of the disease is essential to point out measures that take effect. In this study, the influence of 14 variables was assessed together by Artificial Neural Networks (ANN) of the type Self-Organizing Maps (SOM), to verify the relationship between numbers of cases and deaths from COVID-19 in Brazilian states for 110 days. The SOM analysis showed that the variables that presented a more significant relationship with the numbers of cases and deaths by COVID-19 were influenza vaccine applied, Intensive Care Unit (ICU), ventilators, physicians, nurses, and the Human Development Index (HDI). In general, Brazilian states with the highest rates of influenza vaccine applied, ICU beds, ventilators, physicians, and nurses, per 100,000 inhabitants, had the lowest number of cases and deaths from COVID-19, while the states with the lowest rates were most affected by the disease. According to the SOM analysis, other variables such as Personal Protective Equipment (PPE), tests, drugs, and Federal funds, did not have as significant effect as expected.

Physical Activity-Related Profiles of Female Sixth-Graders Regarding Motivational Psychosocial Variables: A Cluster Analysis Within the CReActivity Project.

IntroductionAdolescents’ physical activity (PA) behavior can be driven by several psychosocial determinants at the same time. Most analyses use a variable-based approach that examines relations between PA-related determinants and PA behavior on the between-person level. Using this approach, possible coexistences of different psychosocial determinants within one person cannot be examined. Therefore, by applying a person-oriented approach, this study examined (a) which profiles regarding PA-related psychosocial variables typically occur in female sixth-graders, (b) if these profiles deliver a self-consistent picture according to theoretical assumptions, and (c) if the profiles contribute to the explanation of PA.Materials and MethodsThe sample comprised 475 female sixth-graders. Seventeen PA-related variables were assessed: support for autonomy, competence and relatedness in PE as well as their satisfaction in PE and leisure-time; behavioral regulation of exercise (five subscales); self-efficacy and social support from friends and family (two subscales). Moderate-to-vigorous PA was measured using accelerometers. Data were analyzed using the self-organizing maps (SOM) analysis, a cluster analysis including an unsupervised algorithm for non-linear models.ResultsAccording to the respective level of psychosocial resources, a positive, a medium and a negative cluster were identified. This superordinate cluster solution represented a self-consistent picture that was in line with theoretical assumptions. The three-cluster solution contributed to the explanation of PA behavior, with the positive cluster accumulating an average of 6 min more moderate-to-vigorous PA per day than the medium cluster and 10 min more than the negative cluster. Additionally, SOM detected a subgroup within the positive cluster that benefited from a specific combination of intrinsic and external regulations with regard to PA.DiscussionThe results underline the relevance of the assessed psychosocial determinants of PA behavior in female sixth-graders. The results further indicate that the different psychosocial resources within a given person do not develop independently of one another, which supports the use of a person-oriented approach. In addition, the SOM analysis identified subgroups with specific characteristics, which would have remained undetected using variable-based approaches. Thus, this approach offers the possibility to reduce data complexity without overlooking subgroups with special demands that go beyond the superordinate cluster solution.

Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification

To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management.

Key influence of clusters of Geldart Group B particles in a circulating fluidized bed riser

The clustering phenomenon is an important characteristic of fluidized bed systems, so much attention has been given to understanding such unstable, transient features through both experiments and simulations. A review has pointed out that, because of the interplay of multiple factors, relationships are at times unclear even within the same study. For such non-linear and multi-dimensional problems, machine learning tools are proficient. In this study, self-organizing map (SOM) analysis was harnessed to classify 1188 circulating fluidized bed (CFB) riser cluster datasets of Geldart Group B particles into potential smaller data assemblies, in order to determine the key influence(s) responsible for the demarcation. Two distinct data assemblies were identified, with one constituted by the monodisperse particle systems (i.e., three narrow particle size distributions (PSDs)), while the other by the non-monodisperse particle systems (i.e., two binary mixtures and one broad PSD). Specifically, the clusters formed by the non-monodisperse systems were distinctively smaller than those of monodisperse ones. This suggests that multiple particle types hindered the growth of clusters, which has been tied to hydrodynamic screening, unequal charging and unequal damping effects that are unique to particle mixtures. More studies are needed to unveil the underlying mechanisms of such different clusters between the monodisperse versus non-monodisperse particle systems.

Searching for lichen indicator species: the application of self-organizing maps in air quality assessment-a case study from Balkan area (Serbia).

The subject of this paper is the possibility of using self-organizing map (SOM) in the biomonitoring studies. We used lichens as biomonitors to indicate different degrees of air quality. This research included all of 88 lichen species that was collected at 75 investigated points. These lichen species showed the different responses to air pollution. The air quality was assessed by IAP (index of atmospheric pollution) values. The IAP values were calculated for all of investigated points on the territory of four natural and one urban ecosystem. Calculated IAP values were in range of 10 to 75. On the basis of the lichen data and IAP values, we have employed SOM analysis that distinguished three clusters (A, B, and C). It presented lichen indicator species for each cluster: 16 species for cluster A, 18 species for cluster B, and two species for cluster C. This paper presents a useful method to find indicator species.

Assessment of Multipollutant Exposures During Pregnancy Using Silicone Wristbands.

Silicone wristbands can assess multipollutant exposures in a non-invasive and minimally burdensome manner, which may be suitable for use among pregnant women. We investigated silicone wristbands as passive environmental samplers in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. We used wristbands to assess exposure to a broad range of organic chemicals, identified multipollutant exposure profiles using self-organizing maps (SOMs), and assessed temporal consistency and determinants of exposures during pregnancy. Participants (n = 255) wore wristbands for 1 week at 12 gestational weeks. Of 1,530 chemicals assayed, 199 were detected in at least one wristband and 16 were detected in >60% of wristbands. A median of 23 (range: 12,37) chemicals were detected in each wristband, and chemicals in commerce and personal care products were most frequently detected. A subset of participants (n=20) wore a second wristband at 24 gestational weeks, and concentrations of frequently detected chemicals were moderately correlated between time points (median intraclass correlation: 0.22; range: 0.00,0.69). Women with higher educational attainment had fewer chemicals detected in their wristbands and the total number of chemicals detected varied seasonally. Triphenyl phosphate concentrations were positively associated with nail polish use, and benzophenone concentrations were highest in summer. No clear associations were observed with other a priori relations, including certain behaviors, season, and socioeconomic factors. SOM analyses revealed 12 profiles, ranging from 2 to 149 participants, captured multipollutant exposure profiles observed in this cohort. The most common profile (n = 149) indicated that 58% of participants experienced relatively low exposures to frequently detected chemicals. Less common (n ≥ 10) and rare (n < 10) profiles were characterized by low to moderate exposures to most chemicals and very high and/or very low exposure to a subset of chemicals. Certain covariates varied across SOM profile membership; for example, relative to women in the most common profile who had low exposures to most chemicals, women in the profile with elevated exposure to galaxolide and benzyl benzoate were younger, more likely to be single, and more likely to report nail polish use. Our study illustrates the utility of silicone wristbands for measurement of multipollutant exposures in sensitive populations, including pregnant women.

Effect of Different Direct Compaction Grades of Mannitol on the Storage Stability of Tablet Properties Investigated Using a Kohonen Self-Organizing Map and Elastic Net Regression Model.

This study tested 15 direct compaction grades to identify the contribution of different grades of mannitol to the storage stability of the resulting tablets. After preparing the model tablets with different values of hardness, they were stored at 25 °C, 75% relative humidity for 1 week. Then, measurement of the tablet properties was conducted on both pre- and post-storage tablets. The tablet properties were tensile strength (TS), friability, and disintegration time (DT). The experimental data were analyzed using a Kohonen self-organizing map (SOM). The SOM analysis successfully classified the test grades into three distinct clusters having different changes in the behavior of the tablet properties accompanying storage. Cluster 1 showed an obvious rise in DT induced by storage, while cluster 3 showed a substantial change in mechanical strength of the tablet including a reduction in the TS and a rise in friability. Furthermore, the data were analyzed using an Elastic net regression technique to investigate the general relationships between the powder properties of mannitol and the change behavior of the tablet properties. Consequently, we succeeded in identifying the crucial powder properties for the storage stability of the resulting tablets. This study provides advanced technical knowledge to characterize the effect of different direct compaction grades of mannitol on the storage stability of tablet properties.

Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM, PMF and GIS methods

Rapid urbanization and industrial growth have triggered heavy metal contamination in agricultural soil in Dhaka, which is a serious concern for ecological risk and public health issues. In this study, fifty-four soil samples from agricultural lands of Dhaka had been analyzed for assessing accumulation, spatial enrichment, ecological risk and sources apportionment of heavy metals using a combined approach of self-organizing map (SOM), positive matrix factorization (PMF), geographical information system (GIS), and enrichment factor (EF). The results of the enrichment factor, geoaccumulation index and contamination factor index showed that more than 90% of the soil samples were polluted by higher levels of Cr and Cd. The mean pollution load index (PLI) results demonstrated that about 73% of soil samples were moderately polluted by heavy metals. Based on SOM and PMF analysis, four potential sources of heavy metals were found in this study area: (i) agrochemical and sewage irrigation (Cd–As); (ii) combined effect of agriculture, industrial and natural sources (Mn, Co, Ni and Zn); (iii) atmospheric deposition and industrial emission (As–Pb); (iv) chemical and leather tanning industries (Cr). The ecological risk index demonstrated that in terms of Cd content, about 75% of soil samples were moderate to high risk, and 20% were moderate to considerable ecological risk, which was the serious environmental, ecological, and public health concern. The spatial projection of ecological risk values showed that the southern part of Dhaka (Keraniganj Upazila) is a high ecological risk in terms of heavy metal pollution. These risk maps in agricultural soils may play a vital role in reducing pollution sources; so that zonal pollution control, as well as ecological protection, may be achieved in this resource-based agricultural land.

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Numerical models and remote sensing observation systems such as radars are useful for providing information on surface flows for coastal areas. Evaluation of their performance and extracting synoptic characteristics are challenging and important tasks. This research aims to investigate synoptic characteristics of surface flow fields through undertaking a detailed analysis of model results and high frequency radar (HFR) data using self-organizing map (SOM) and empirical orthogonal function (EOF) analysis. A dataset of surface flow fields over thirteen days from these two sources was used. A SOM topology map of size 4 × 3 was developed to explore spatial patterns of surface flows. Additionally, comparisons of surface flow patterns between SOM and EOF analysis were carried out. Results illustrate that both SOM and EOF analysis methods are valuable tools for extracting characteristic surface current patterns. Comparisons indicated that the SOM technique displays synoptic characteristics of surface flow fields in a more detailed way than EOF analysis. Extracted synoptic surface current patterns are useful in a variety of applications, such as oil spill treatment and search and rescue. This research provides an approach to using powerful tools to diagnose ocean processes from different aspects. Moreover, it is of great significance to assess SOM as a potential forecasting tool for coastal surface currents.

Conceptual Model of Arsenic Mobility in the Shallow Alluvial Aquifers Near Venice (Italy) Elucidated Through Machine Learning and Geochemical Modeling

AbstractThis work proposed a novel method to elucidate the controls of As mobility in complex aquifers based on an unsupervised machine learning algorithm, Self‐Organizing Map (SOM), and process‐based geochemical modeling. The approach is tested in the shallow aquifers of the Venetian Alluvial Plain (VAP) near Venice, Italy, where As concentrations seasonally and locally exceed recommended drinking water limits. SOM was fed using information from two geochemical surveys on eight VAP boreholes, and continuous reading of hourly groundwater head levels and weekly geochemical analyses from three VAP boreholes between mid‐October 2017 and end of January 2018. The SOM analysis is consistent with redox‐controlled dissolution‐precipitation hydrous ferric oxides (HFOs) as a key control of As mobility in the aquifer. Dissolved As is positively correlated to Fe and and negatively to the oxidizing‐reducing potential (ORP). Negative correlation between As and groundwater head levels suggests a redox control by rainfall‐driven recharge, which adds oxidants to the aquifer while progressively attenuating As. This mechanism is tested using process‐based geochemical modeling, which simulates different transport modalities of oxidants entering the aquifer. Starting from reducing aquifer conditions, the model reproduces correctly the observed ORP and the trends in As and Fe, when the function describing the occurrence of oxidizing events scales according to the temporal occurrence of rainfall events. Heterogeneity can strongly control the local‐scale effectiveness of recharge as a natural As attenuating factor, requiring a different model analysis to be properly assessed and to be developed in a follow‐up study.

Rain-on-Snow events in Japan as projected by a large ensemble of regional climate simulations

Rain-on-Snow (ROS) events can cause severe snowmelt hazards such as river flooding, avalanches, and landslides that have significant impacts on various sectors. The influence of climate change on the frequency of ROS events in Japan was investigated using climate projections obtained from the Database for Policy Decision making for Future climate change (d4PDF). The projected future climate in the regional model simulations showed an increase in the ROS events over the mountainous areas in Hokuriku (Sea of Japan side of Central Japan) and Hokkaido (Northern Japan) regions, where a higher amount of snowfall will still occur in the future. Characteristics of ROS events such as rainfall, snowmelt, and related runoff were also enhanced in these regions. Self-organizing maps (SOMs) were applied using the surface atmospheric circulation data to determine the dominant ROS-related weather patterns (WPs) in the present and future climate. The SOMs showed that some WPs had a significant effect on the cause of the ROS events. The differences in the impacts of climate change between the WPs were evaluated to understand the future changes in runoff and snowmelt associated with ROS events. The SOM analysis suggests that the increase in the occurrence of ROS events and the resultant enhancement in their characteristics in the future-climate projection can be attributed to the changes in the dominant ROS-related WPs (from cyclonic to cold-surge type) corresponding to variations in the freezing point line. These findings can inform water hazard and water resource management plans that aim to withstand regional climate change.

Can emojis mean “Earthquake”?

With the increase in digital communications systems and social media, there is a need for simple graphics-based ways to signal the occurrence of major disasters. We describe the development and initial usability testing of a sign to indicate that an earthquake has occurred. We involved 264 Italian adults, who completed an online survey to evaluate the evocativeness, simplicity, and universality of 16 emojis depicting earthquakes. Through a Self-Organising Map analysis we identified four similar response profiles to the evocativeness scale, differing in their mean strength. A non-metric Multidimensional Scaling suggested that evocativeness was higher for the emojis featuring a damaged building. Linear Mixed Models indicated that emojis with fabricated vs. natural structures were judged as more evocative, simpler, and more universal when characterized by danger from falling objects. In some cases, adding behavioral elements increased evocativeness. Practical implications for informing the development of a new emoji for earthquakes are discussed.

Northern Hemisphere Winter Air Temperature Patterns and Their Associated Atmospheric and Ocean Conditions

AbstractThe Northern Hemisphere (NH) has experienced winter Arctic warming and continental cooling in recent decades, but the dominant patterns in winter surface air temperature (SAT) are not well understood. Here, a self-organizing map (SOM) analysis is performed to identify the leading patterns in winter daily SAT fields from 1979 to 2018, and their associated atmospheric and ocean conditions are also examined. Three distinct winter SAT patterns with two phases of nearly opposite signs and a time scale of 7–12 days are found: one pattern exhibits concurrent SAT anomalies of the same sign over North America (NA) and northern Eurasia, while the other two patterns show SAT anomalies of opposite signs between, respectively, NA and the Bering Sea, and the Kara Sea and East Asia (EA). Winter SAT variations may arise from changes in the SOM frequencies. Specifically, the observed increasing trends of winter cold extremes over NA, central Eurasia, and EA during 1998–2013 can be understood as a result of the increasing occurrences of some specific SAT patterns. These SOMs are closely related to poleward advection of midlatitude warm air and equatorward movements of polar cold airmass. These meridional displacements of cold and warm airmasses cause concurrent anomalies over different regions not only in SAT but also in water vapor and surface downward longwave radiation. Anomalous sea surface temperatures in the tropical Pacific, midlatitude North Pacific, and North Atlantic and anomalous Arctic sea ice concentrations also concur to support and maintain the anomalous atmospheric circulation that causes the SAT anomalies.

Intraseasonal variability of the equatorial Pacific Ocean and its relationship with ENSO based on Self-Organizing Maps analysis

We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation (ENSO) using Self-Organizing Maps (SOM) analysis. Variation in intraseasonal subsurface temperature is mainly found along the thermocline. The SOM patterns concentrate in basin-wide seesaw or sandwich structures along an east-west axis. Both the seesaw and sandwich SOM patterns oscillate with periods of 55 to 90 days, with the sequence of them showing features of equatorial intraseasonal Kelvin wave, and have marked interannual variations in their occurrence frequencies. Further examination shows that the interannual variability of the SOM patterns is closely related to ENSO; and maxima in composite interannual variability of the SOM patterns are located in the central Pacific during CP El Nino and in the eastern Pacific during EP El Nino. These results imply that some of the ENSO forcing is manifested through changes in the occurrence frequency of intraseasonal patterns, in which the change of the intraseasonal Kelvin wave plays an important role.

Analysis on herbal medicines utilized for treatment of COVID-19

As coronavirus disease 2019 (COVID-19) pandemic poses a substantial global public health threat, traditional Chinese medicine (TCM) was used in 91.50% of the COVID-19 cases in China, showing encouraging results in improving symptom management and reducing the deterioration, mortality, and recurrence rates. A total of 166 modified herbal formulae consisting of 179 single herbal medicines were collected for treating COVID-19 in China. Glycyrrhizae Radix et Rhizome, Scutellariae Radix, and Armeniacae Semen Amarum are the most frequently utilized in clinics, most of which are antipyretic (47, 26.26%), expectorant and cough-suppressing (22, 12.29%), and dampness-resolving (21, 11.73%) from traditional descriptions. A total of 1212 chemical components containing β-sitosterol, stigmasterol, and quercetin were primarily selected. Additionally, using complex system entropy and unsupervised hierarchical clustering, 8 core herbal combinations and 10 new formulae emerged as potentially useful candidates for COVID-19. Finally, following scaffold analysis, self-organizing mapping (SOM) and cluster analysis, 12 clusters of molecules yielded 8 pharmacophore families of structures that were further screened as pharmacological targets in human metabolic pathways for inhibiting coronavirus. This article aims to make more easily accessible and share historical herbal knowledge used in contemporary treatments in a modern manner to assist researchers contain the global spread of COVID-19.

Impact of the North Atlantic Warming Hole on Sensible Weather

AbstractIn future climate projections there is a notable lack of warming in the North Atlantic subpolar gyre, known as the North Atlantic warming hole (NAWH). In a set of large-ensemble atmospheric simulations with the Community Earth System Model, the NAWH was previously shown to contribute to the projected poleward shift and eastward elongation of the North Atlantic jet. The current study investigates the impact of the warming hole on sensible weather, particularly over Europe, using the same simulations. North Atlantic jet regimes are classified within the model simulations by applying self-organizing maps analysis to winter daily wind speeds on the dynamic tropopause. The NAWH is found to increase the prevalence of jet regimes with stronger and more-poleward-shifted jets. A previously identified transient eddy-mean response to the NAWH that leads to a downstream enhancement of wind speeds is found to be dependent on the jet regime. These localized regime-specific changes vary by latitude and strength, combining to form the broad increase in seasonal-mean wind speeds over Eurasia. Impacts on surface temperature and precipitation within the various North Atlantic jet regimes are also investigated. A large decrease in surface temperature over Eurasia is found to be associated with the NAWH in regimes where air masses are advected eastward over the subpolar gyre prior to reaching Eurasia. Precipitation is found to be locally suppressed over the warming hole region and increased directly downstream. The impact of this downstream response on coastal European precipitation is dependent on the strength of the NAWH.

Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion

This study focused on demonstrating the use of a self-organizing map (SOM) algorithm to elucidate patterns among variables in simulated syngas combustion. The work was implemented in two stages: (1) modelling and simulation of syngas combustion under various feed composition and reactor temperature implemented in AspenPlusTM chemical process simulation software, and (2) pattern recognition among variables using SOM algorithm implemented in MATLAB. The varied levels of feed syngas composition and reactor temperature was randomly sampled from uniform distributions using the Morris screening technique creating four thousand eight hundred simulation conditions implemented in the process simulation which consequently produced a multivariate dataset used in the SOM analysis. Results show that cylindrical SOM topology models the dataset at lower quantization error and topographic error as compared to the rectangular SOM topology indicating suitability of the former for variables pattern elucidation for the simulated combustion. Nonetheless, the variables pattern between component planes from rectangular SOM (9 × 28 grid) and those from cylindrical SOM (9 × 28 grid) are almost similar, indicating that either rectangular or cylindrical architectures may be used for variables pattern analysis. The component planes of process variables from trained SOM are a convenient visualization of the trends across all process variables.

Can the SOM Analysis Predict Business Failure Using Capital Structure Theory? Evidence from the Subprime Crisis in Spain

The paper aims to identify which variables related to capital structure theory predict business failure in the Spanish construction sector during the subprime crisis. An artificial neural network (ANN) approach based on Self-Organizing Maps (SOM) is proposed, which allows one to cluster between default and active firms’ groups. The similarities and differences between the main features in each group determine the variables that explain the capacities of failure of the analyzed firms. The network tests whether the factors that explain leverage, such as profitability, growth opportunities, size of the company, risk, asset structure, and age of the firm, can be suitable to predict business failure. The sample is formed by 152 construction firms (76 default and 76 active) in the Spanish market. The results show that the SOM correctly predicts 97.4% of firms in the construction sector and classifies the firms in five groups with clear similarities inside the clusters. The study proves the suitability of the SOM for predicting business bankruptcy situations using variables related to capital structure theory and financial crises.