Self-organizing Map Method Research Articles

A Framework for Separating Climate and Anthropogenic Contributions to Evapotranspiration Changes in Natural to Agricultural Regions of Watersheds Based on Machine Learning

Evapotranspiration is a crucial component of the water cycle and is significantly influenced by climate change and human activities. Agricultural expansion, as a major aspect of human activity, together with climate change, profoundly affects regional ET variations. This study proposes a quantification framework to assess the impacts of climate change (ETm) and agricultural development (ETh) on regional ET variations based on the Random Forest algorithm. The framework was applied in a large-scale agricultural expansion area in China, specifically, the Songhua River Basin. Meteorological, topographic, and ET remote sensing data for the years of 1980 and 2015 were selected. The Random Forest model effectively simulates ET in the natural areas (i.e., forest, grassland, marshland, and saline-alkali land) in the Songhua River Basin, with R2 values of around 0.99. The quantification results showed that climate change has altered ET by −8.9 to 24.9 mm and −3.4 to 29.7 mm, respectively, in the natural areas converted to irrigated and rainfed agricultural areas. Deducting the impact of climate change on the ET variation, the development of irrigated and rainfed agriculture resulted in increases of 2.9 mm to 55.9 mm and 0.9 mm to 53.4 mm in ET, respectively, compared to natural vegetation types. Finally, the Self-Organizing Map method was employed to explore the spatial heterogeneity of ETh and ETm. In the natural–agriculture areas, ETm is primarily influenced by moisture conditions. When moisture levels are adequate, energy conditions become the predominant factor. ETh is intricately linked not only to meteorological conditions but also to the types of original vegetation. This study provides theoretical support for quantifying the effects of climate change and farmland development on ET, and the findings have important implications for water resource management, productivity enhancement, and environmental protection as climate change and agricultural expansion persist.

Seasonal evolution modes of tropical sea surface temperature anomalies and their links to antarctic sea ice anomalies

Abstract Previous studies have explored the teleconnections between variability of Antarctic sea ice cover and tropical sea surface temperature (SST) across the Pacific, Atlantic, and Indian Ocean basins, typically focusing on each basin individually. However, there has been limited investigation into the impact of tropical SSTs—particularly from a seasonal evolution perspective—on Antarctic sea ice cover. In this study, we employ the self-organizing map method to identify and analyze the primary modes of seasonal SST evolution in the tropical oceans from 1854 to 2022. We also project changes in the frequency of these modes through the 21st century. Moreover, we examine the seasonal variability of Antarctic sea ice concentration in relation to these tropical SST modes over the past four decades. Our results reveal that tropical SST anomalies display both uniform and shifted seasonal evolution patterns. Notably, the frequency of switched modes—namely, transitions from La Niña to El Niño (node 8) and from El Niño to La Niña (node 3)—is expected to increase in future climate. Interestingly, nearly mirrored SST seasonal evolution patterns do not lead to entirely opposite atmospheric circulation anomalies in the southern mid-high latitudes, nor do they result in completely inverse Antarctic sea ice cover anomalies.

Classification models and SAR analysis of anaplastic lymphoma kinase (ALK) inhibitors using machine learning algorithms with two data division methods.

Anaplastic lymphoma kinase (ALK) plays a critical role in the development of various cancers. In this study, the dataset of 1810 collected inhibitors were divided into a training set and a test set by the self-organizing map (SOM) and random method, respectively. We developed 32 classification models using Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) to distinguish between highly and weakly active ALK inhibitors, with the inhibitors represented by MACCS and ECFP4 fingerprints. Model 7D which was built by the RF algorithm using training set 1/test set 1 divided by the SOM method, provided the best performance with a prediction accuracy of 90.97% and a Matthews correlation coefficient (MCC) value of 0.79 on the test set. We clustered the 1810 inhibitors into 10 subsets by K-Means algorithm to find out the structural characteristics of highly active ALK inhibitors. The main scaffolds of highly active ALK inhibitors were also analyzed based on ECFP4 fingerprints. It was found that some substructures have a significant effect on high activity, such as 2,4-diarylaminopyrimidine analogues, pyrrolo[2,1-f][1,2,4]triazin, indolo[2,3-b]quinoline-11-one, benzo[d]imidazol and pyrrolo[2,3-b]pyridine. In addition, the subsets were summarized into several clusters, among which four clusters showed a significant relationship with ALK inhibitory activity. Finally, Shapley additive explanations (SHAP) was also used to explain the influence of modeling features on model prediction results. The SHAP results indicated that our models can well reflect the structural features of ALK inhibitors.

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Context. Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. Aims. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. Methods. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. Results. The findings demonstrate a high agreement level (∼70–90%) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry, and CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single–binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than ∼700 nm. Conclusions. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

Study of tropical cyclone wave characteristics based on a hybrid track clustering method

Understanding the activity and wave characteristics of different TC track types with different formation mechanisms is crucial for coastal and ocean engineering. In this study, a hybrid clustering method based on track indicators representing track shape and influence region is proposed to classify 721 TC tracks along the coast of China into three track types. Due to the intrinsic similarity of track indicators diagnosed by the self-organizing map method, the initial clustering centroid selection of K-means method is reasonable. 5000 clustering trials show that the clustering results are more robust than those of the traditional K-means method, and the average difference percentage decreases from 11.3% to 5.5%. By analyzing the robust clustering results, the activity characteristics of three TC types under the background of atmospheric circulation patterns and the characteristics of the waves generated by the three TC types are studied, especially the characteristics of extreme waves. The joint distribution pattern of extreme wave height and wave direction indicates the direction sector shift and frequency change caused by different TC types. These results provide important insights for risk analysis and resilience construction of coastal and ocean engineering structures.

Delineating Ecological Functional Zones and Grades for Multi-Scale Ecosystem Management

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is fundamental in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a “two-step refinement zoning method” to address this gap, first using a self-organizing feature mapping method to delineate EFZs at a township scale, and then applying a hotspot overlay analysis to refine the resulting EFZs by designating them with different grades at the village scale. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ES combinations and land use compositions. Furthermore, 5.23% of villages were identified as level I areas of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management.

Relationship between interannual variability of the north edge of the East Asian summer monsoon and extreme precipitation in North China

The north edge of the East Asian summer monsoon (EASM) is defined by using the isoline with average total column water vapor (TCWV) equal to 25 mm, and the relationship between the north edge of the EASM and summer extreme precipitation in North China (NC) from 1961 to 2020 is discussed. Self-Organizing Maps (SOM) method is used to cluster extreme precipitation in the abnormal years of the north edge in NC, and the circulation characteristics of different types of extreme precipitation are discussed. The results show that the north edge of the EASM is closely related to extreme precipitation in NC. When the north edge is northerly, both the frequency and total amount of extreme precipitation increase significantly in the monsoon marginal zone. Based on different monsoon circulation background, summer extreme precipitation events in NC are divided into four categories, namely, the central type and southern type in the northerly years of north edge, and the central type and eastern type in the southerly years of north edge. The occurrence of different types of extreme precipitation is closely related to the different characteristics of local trough, the western Pacific subtropical high (WPSH), and wave trains in the middle and high latitudes. Specifically, the central type extreme precipitation is affected by the mid-latitude wave train in the northerly years of north edge. As for the central type extreme precipitation in the southerly years of north edge, both high-latitude and mid-latitude wave trains transfer from west to east and converge over NC. However, the wave disturbance energy is relatively weak, leading to the extreme precipitation with weak intensity.

Increase in porosity and permeability resolution for thin-bedded Miocene formation in Carpathian Foredeep using different clustering methods

AbstractThe accurate interpretation of well-logging data is a crucial stage in the exploration of gas- and oil-bearing reservoirs. Geological formations, such as the Miocene deposits, present many challenges related to thin layers, whose thickness is often less than the measurement resolution. This research emphasizes the potential of utilizing electrofacies in such challenging environments. The application of electrofacies not only allows for the grouping of intervals with similar physical characteristics but can also be useful for estimating porosity and permeability parameters. For this purpose, various clustering methods were tested, including the 2D indexed and probabilized self-organizing map (IPSOM) method with and without supervision. Subsequently, the usefulness of the obtained results to improve the estimation of porosity and permeability parameters with the help of artificial neural networks was verified. As a result of the conducted analyses, significantly better results were obtained compared to classical petrophysical interpretation. The calculated porosity and permeability parameters were characterized by much greater variability and alignment with laboratory measurements on porosity and permeability. The best results were obtained for the IPSOM method, but the other methods did not differ significantly. In conclusion, the studies have shown a positive result of applying clustering methods, including the IPSOM method, to improve the estimation of permeability and porosity parameters in complicated, thinly-layered formations.

Assessing Sustainable Development in Community Welfare and Economic Resilience to Extreme Weather in Indonesia

In recent decades, Indonesia has experienced a surge in natural disasters, resulting in increased casualties and disruptions to economic growth and welfare. This study investigates the impact of various types of natural disasters, focusing on how economic growth (measured by provincial GDP) and welfare levels (measured by the Human Development Index, HDI) influence the number of victims affected by extreme weather. Data on gross regional domestic product and the Human Development Index for each province in Indonesia were obtained from Statistics Indonesia. We employed multivariable negative binomial regression to analyze the relationships between the number of victims affected by extreme weather, provincial HDI, and provincial GDP. The results indicate significant correlations between provincial GDP, HDI, and the number of victims. Higher HDI correlates with fewer victims, whereas higher GDP is associated with an increase in victims. Additionally, we used the Self-Organizing Map (SOM) method, identifying two clusters as the optimal model. Cluster 1 comprises 31 provinces, while Cluster 2 comprises 3 provinces, with the latter highlighting the provinces with the highest disaster risk. Consequently, provinces such as West Java, Central Java, and East Java require heightened attention from various stakeholders involved in disaster management efforts. By examining these relationships, our study contributes to the understanding of sustainable development and resilience against natural disasters. It underscores the importance of improving welfare and economic policies to mitigate the impacts of extreme weather events.

Evaluation of input variable selection methods in artificial neural networks for estimating missing daily precipitation

ABSTRACT This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation.

Identifying the genetic mechanism of medium-low temperature fluoride-enriched geothermal groundwater by the self-organizing map and evaluating health risk in the Wugongshan area, southeast China.

Fluoride-enriched groundwater is a serious threat for groundwater supply around the world. The medium-low temperature fluoride-enriched geothermal groundwater resource is widely distributed in the circum-Wugongshan area. And the fluoride concentration of all geothermal samples exceeds the WHO permissible limit of 1.5mg/L. The Self-Organizing Map method, hydrochemical and isotopic analysis are used to decipher the driving factors and genetic mechanism of fluoride-enriched geothermal groundwater. A total of 19 samples collected from the circum-Wugongshan geothermal belt are divided into four clusters by the self-organizing map. Cluster I, Cluster II, Cluster III, and Cluster IV represent the geothermal groundwater with the different degree of fluoride concentration pollution, the different hydrochemical type, and the physicochemical characteristic. The high F- concentration geothermal groundwater is characterized by HCO3-Na with alkalinity environment. The δD and δ18O values indicate that the geothermal groundwater origins from the atmospheric precipitation with the recharge elevation of 1000-2100m. The dissolution of fluoride-bearing minerals is the main source of fluoride ions in geothermal water. Moreover, groundwater fluoride enrichment is also facilitated by water-rock interaction, cation exchange and alkaline environment. Additionally, the health risk assessment result reveals that the fluorine-enriched geothermal groundwater in the western part of Wugongshan area poses a more serious threat to human health than that of eastern part. The fluoride health risks of geothermal groundwater for different group show differentiation, 100% for children, 94.74% for adult females, and 68.42% for adult males, respectively. Compared with adult females and adult males, children faced the greatest health risks. The results of this study provide scientific evaluation for the utilization of geothermal groundwater and the protection of human health around the Wugongshan area.

Detection of the irrotational boundary using machine learning methods

Four machine learning methods, i.e., self-organizing map (SOM), Gaussian mixture model (GMM), eXtreme gradient boosting (XGBoost), and contrastive learning (CL), are used to detect the irrotational boundary (IB), which represents the outer edge of the turbulent and non-turbulent interface layer. To accurately evaluate the detection methods, high-resolution databases from direct numerical simulations of a temporally evolving turbulent plane jet are used. It is found that except for the SOM method, the general contour of the IB appears to be effectively captured using the GMM, XGBoost, and CL methods, which indicate the turbulent and non-turbulent regions can be roughly recognized. Furthermore, the intrinsic features of the detected IB using the GMM, XGBoost, and the CL methods are quantitatively evaluated. Unlike the conventional vorticity norm method, the three machine learning methods do not rely on a single threshold of vorticity magnitude to separate the turbulent and non-turbulent regions. A small part of the detected IB using the three machine learning methods is characterized by the rotational motions, which are expected to be only found inside the turbulent sublayer and turbulent core region. Compared to the vorticity norm and XGBoost methods, the fractal dimensions of the IB detected by the GMM and CL methods are relatively small, which are related to the missing detection of some highly contorted elements. With the three machine learning methods, a large part of the detected IB is characterized by a convex shape, similarly as with the vorticity norm. However, the probability density function profiles of the local curvature of the detected IB differ greatly between the three machine learning methods and the vorticity norm. A mild variation of the mean conditional distributions of the vorticity magnitude can be observed across the detected IB by the three machine learning methods. This study first implies that using the machine learning methods the turbulent and non-turbulent regions can be roughly distinguished, but it is still challenging to obtain the intrinsic features of the detected IB.

Construction of regional multi-scenario spatially balanced ecological security pattern based on self-organizing feature map: A case study of Guangzhou, China

Constructing ecological security patterns (ESPs) is the ecological basis for achieving sustainable regional development. This study explored a framework of spatially balanced ESPs suitable for rapidly urbanizing areas to address the issues of overreliance on ecological land and subjective parameter settings in traditional ESP construction methods. Three ecosystem service bundles (product supply, water conservation, and habitat maintenance) were identified in Guangzhou using the self-organizing feature map method. These bundles were then used to identify multifunctional sources. The minimum size of the ecological source was determined to be 10 km2 through the “structure-pattern-function” framework, and a total of 19 ecological sources from three ecological functions were extracted. In these three scenarios, the 102 ecological corridors were classified into five categories based on their connected sources. Ecological node parameters were set based on local planning and dominant species characteristics, enhancing the explanatory power of the circuit theory. The multi-scenario ESP framework proposed in this study achieved a spatially balanced distribution of the entire region at the point, line, and surface levels, as confirmed by the ecological network assessment results. Future planning in Guangzhou should value the ecological functions of non-conventional ecological land, such as cropland and urban green spaces, while considering the impact of human activities on the environment and promptly modifying crucial node areas.

A dynamic correction method for the optimal value settings of the solution purification process at multiple time scales

The solution purification process includes multiple continuous reactors. Setting the key technical indicators of each reactor through global optimization is the prerequisite for realizing the optimal operation of the entire process. Affected by fluctuations in inlet conditions, adjustments of operating parameters, and random disturbances, the operating status of the solution purification process will change accordingly, causing the optimal value settings based on global optimization to become no longer applicable. To ensure the applicability of the optimal value settings as the process changes and considering that the production data collected at different time scales contain different process information, this study proposes a dynamic correction method for the optimal value settings of the solution purification process at multiple time scales. First, considering the low-frequency testing data that can reflect the operation effect, the low-frequency correction is realized by combining mechanism knowledge and expert experience. Second, based on the characteristic that the high-frequency detection data can reflect the changing operating status in time, a supervised self-organizing map method is proposed to classify the changing trends in the operating status. Finally, an integrated, spatiotemporal, just-in-time learning method (with multiple changing trends in the operating status) is proposed to realize high-frequency correction. The experimental results show that the proposed method can dynamically correct the optimal value settings and reduce resource consumption while ensuring product quality.

Patterns of infant fecal metabolite concentrations and social behavioral development in toddlers.

Gut-derived metabolites, products of microbial and host co-metabolism, may inform mechanisms underlying children's neurodevelopment. We investigated whether infant fecal metabolites were related to toddler social behavior. Stool samples collected from 6-week-olds (n = 86) and 1-year-olds (n = 209) in the New Hampshire Birth Cohort Study (NHBCS) were analyzed using nuclear magnetic resonance spectroscopy metabolomics. Autism-related behavior in 3-year-olds was assessed by caregivers using the Social Responsiveness Scale (SRS-2). To assess the association between metabolites and SRS-2 scores, we used a traditional single-metabolite approach, quantitative metabolite set enrichment (QEA), and self-organizing maps (SOMs). Using a single-metabolite approach and QEA, no individual fecal metabolite or metabolite set at either age was associated with SRS-2 scores. Using the SOM method, fecal metabolites of six-week-olds organized into four profiles, which were unrelated to SRS-2 scores. In 1-year-olds, one of twelve fecal metabolite profiles was associated with fewer autism-related behaviors, with SRS-2 scores 3.4 (95%CI: -7, 0.2) points lower than the referent group. This profile had higher concentrations of lactate and lower concentrations of short chain fatty acids than the reference. We uncovered metabolic profiles in infant stool associated with subsequent social behavior, highlighting one potential mechanism by which gut bacteria may influence neurobehavior. Differences in host and microbial metabolism may explain variability in neurobehavioral phenotypes, but prior studies do not have consistent results. We applied three statistical techniques to explore fecal metabolite differences related to social behavior, including self-organizing maps (SOMs), a novel machine learning algorithm. A 1-year-old fecal metabolite pattern characterized by high lactate and low short-chain fatty acid concentrations, identified using SOMs, was associated with social behavior less indicative of autism spectrum disorder. Our findings suggest that social behavior may be related to metabolite profiles and that future studies may uncover novel findings by applying the SOM algorithm.

Spatial and temporal patterns of fish assemblages in Lake Nokoué and the Porto-Novo Lagoon (Benin, West Africa)

Abstract Fish distribution in the lagoon complex of Lake Nokoué and Porto-Novo Lagoon is poorly documented. The lagoon complex is of particular interest because of its contribution to the local supply of animal protein. The fish communities in the complex were studied monthly between January and December 2015 at six sampling sites using two sets of gillnets with varying mesh sizes. The study applied a non-linear self-organizing map (SOM) method to classify the sampling sites based on the composition of fish assemblages, and an index of indicator value (IndVal) was calculated for each species identified. In total, 58 species from 49 genera and 34 families were cataloged with 45 species observed in Lake Nokoué and 48 in the Porto-Novo Lagoon. Notably, 35 species were common to both water bodies. The analysis revealed a dominance of occasional continental (20.7%) and strict estuarine species (20.7%). The SOM analysis suggested a tripartite structure of the fish communities, largely influenced by the geographical positioning of the sampling sites. The IndVal method identified 28 indicator species in the typical assemblages delineated by the SOM approach. The study highlights the importance of these indicator species for the conservation efforts aimed at sustaining the ichthyofauna of the lagoon complex.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42− while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3−. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl−, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43–0.70, 0.16–0.49 and 0.25–0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Wastewater recycling and groundwater sustainability through self-organizing map and style based generative adversarial networks

Wastewater recycling is a pivotal strategy in sustainable water management, designed to mitigate water scarcity and curb environmental pollution. This research introduces an innovative approach merging Self-Organizing Maps (SOM) and Style-Based Generator Generative Adversarial Networks (StyleGAN) to revolutionize wastewater recycling. SOM optimizes the distribution of treated water by identifying ideal locations for recycling outlets and treatment facilities. Meanwhile, StyleGAN enhances water quality by learning from diverse samples, producing purified water suitable for non-drinking purposes. The combination of SOM and StyleGAN maximizes resource utilization and addressing water scarcity challenges while minimizing environmental impact. Also, this method is not only optimizes treated water distribution but also enhances water quality, showcasing potential benefits for sustainable water practices, including groundwater replenishment. Self-Organizing Maps construct a spatial model of the wastewater treatment system, finding optimal locations for recycling outlets and treatment facilities. This spatial intelligence optimizes the distribution of treated water, channeling it efficiently to high-demand areas, thereby maximizing resource utilization. Simultaneously, Style-Based Generator Generative Adversarial Networks elevate the quality of treated wastewater. By assimilating knowledge from diverse wastewater samples, StyleGAN produces water of superior quality with diminished contaminants and enhanced aesthetics. This transformation renders treated wastewater more versatile for non-potable purposes like irrigation and industrial processes. The SOM-StyleGAN technologies offers a holistic solution for wastewater recycling, addressing both distribution efficiency and water quality enhancement. The SOM method outperforms MLP, DBN, and GAN-ANN in fidelity, diversity, contaminant reduction, and aesthetic quality. With a fidelity of 0.97, substantial improvements over GAN-ANN showcase its effectiveness. These results underscore the method's potential contribution to sustainable water management practices, emphasizing its versatility and quality in treated water generation. These findings contribute significantly to enhancing water quality and underscore its potential practical implications for sustainable water practices, marking a substantial step forward in the field.

Redes neurais artificiais para descritores morfológicos em germoplasma de alface biofortificada

ABSTRACT The classification based on morphological descriptors in lettuce is considered a complex activity and proves to be efficient for studying phenotypic characteristics. Therefore, the objective of this study was to analyze the biofortified lettuce germplasm bank at the Universidade Federal de Uberlândia using both conventional morphological descriptors and artificial neural networks. The experiment was conducted in the field. The experimental design employed was a randomized complete block design, consisting of 14 treatments (11 genotypes of mini lettuce, and the cultivars Purpurita, UDI 10.000, and Pira 72) with four replications. Nine morphological descriptors were evaluated. Following the data acquisition, dissimilarity matrix analyses, principal component analysis, dendrogram construction, and artificial neural network (ANN) analyses were performed. The genotypes exhibited phenotypic variability when compared to the parental strains UDI 10.000 and Pira 72. The purple color of the leaves and anthocyanin presence across the entire leaf surface were predominant among the genotypes. Descriptors such as leaf intensity and color, as well as anthocyanin intensity, coloration, and distribution, were the most influential in assessing genetic variability. The Self-Organizing Map (SOM) demonstrated greater sensitivity in discriminating between genotypes compared to the Unweighted Pair Group Method with Arithmetic Mean (UPGMA). While the UPGMA clustering method grouped genotypes into three clusters, the SOM method grouped into five clusters. The use of genetic distance analyses and SOM dendrogram proved to be effective in selecting individuals UFU 215#1, UFU 215#2, UFU 215#6, UFU 215#10, and UFU 215#13, which are clustered with the cultivar UFU Mini Biofort.

OECD Ülkelerinin Sürdürülebilir Kalkınma Değişkenlerine Göre Kendi Kendine Öğrenen Haritalar Yaklaşımı ile Kümelenmesi

Sustainable Development concept (SD) aims to better life for future generations. However, the COVID-19 pandemic has caused tremendous effects on people’s life in several areas. Therefore, the study aimed to investigate the impact of COVID-19 on the selected part of SD indicators in the OECD countries using Self-Organizing Map (SOM). SOM is a kind of artificial neural network (ANN) method, which is an effective clustering method to find hinder non-linear relationships between indicators. The data contained 38 OECD member countries for 11 variables for each country, covering three years (2019-2021). Firstly, descriptive statistics and Spearman rank correlation analysis were used for bivariate analysis. The coefficient of variation was also used to measure the convergence of indicators. Then, it was a two-stage clustering method using SOM and hierarchical clustering methods—the optimal cluster found according to the Silhouette Index and Davies–Bouldin Index, and as three. The convergence of gross domestic product increased gradually to 40.33% in 2019, 42.01% in 2020, and 43.69% in 2021, meaning increasing relative variability of OECD countries. While the mean of the life span was decreased, the share of health expenditure, health expenditure per capita, out-of-pocket health expenditure, and government health expenditure were increased in the study period. According to clustering analysis, the countries had similar characteristics within three years, except Colombia. Also, the USA distinguished very different characteristics from other OECD countries. Although the mean of study indicators varies due to the effect of the pandemic, the change within each OECD country showed mostly similar characteristics within three years.