Self-organizing Map Research Articles

Retraction notice: A novel approach for brain tumor detection by self-organizing map (SOM) using adaptive network based fuzzy inference system (ANFIS) for robotic systems

Retraction notice: A novel approach for brain tumor detection by self-organizing map (SOM) using adaptive network based fuzzy inference system (ANFIS) for robotic systems

Enhancing Ovarian Cancer Detection Using Self-Organizing Maps and Improved Recurrent Neural Networks with Extended Harmony Search Optimization

Background: Ovarian cancer (OC) is a highly fatal malignancy of the female reproductive system, characterized by its high mortality rate and the challenges associated with clinical research due to the disease's complexity and late-stage diagnosis. Advances in technology, such as the Internet of Medical Things (IoMT), offer new opportunities for improving OC detection and diagnosis. Objective: This study aimed to develop and evaluate a novel method for OC detection using IoMT data, leveraging Self-Organizing Maps (SOM) and Improved Recurrent Neural Networks (IRNN) enhanced with the Extended Harmony Search Optimization (EHSO) algorithm to improve feature selection and classification accuracy. Methods: The study utilized OC data from the IoMT and applied SOM for feature selection, which helps in managing and classifying large datasets. SOM was employed to improve data representation and address challenges in labeling and classifying data. The IRNN model, optimized using the EHSO algorithm, was developed to enhance classification performance. The model was tested using a dataset from Kaggle comprising 179 benign and 172 malignant OC images with 50 attributes. Results: The IRNN model with EHSO demonstrated superior performance compared to other methods. In the training dataset, it achieved an accuracy of 95.72% and a Root Mean Square Error (RMSE) of 4.8%. For the testing dataset, the model maintained a high accuracy of 90.4% and an RMSE of 6.8%. The IRNN with EHSO outperformed alternative methods in terms of specificity and sensitivity, while the Genetic Algorithm (GA) showed the lowest performance across all metrics. Conclusion: The proposed method using SOM and IRNN with EHSO significantly improves the detection of ovarian cancer by optimizing feature selection and classification accuracy. This approach offers a promising advancement in utilizing IoMT data for early and accurate OC diagnosis, potentially enhancing patient outcomes through more effective detection and treatment strategies.

Augmented Reality-Based Chronic Wound Healing Assessment Using 3D Stereoscopic Imaging and Self-Organizing Maps

Background: Chronic wounds (CW) present significant public health challenges, impacting individuals' well-being and daily activities. Traditional wound measurement methods, such as the ruler technique, are time-consuming, imprecise, and pose infection risks. Advances in wound care technology, including artificial intelligence (AI) and augmented reality (AR), have improved wound healing (WH) assessment accuracy and enhanced clinician-patient communication. Methods: This study developed a novel CW evaluation device combining AR and a stereoscopic camera system. The device captures images using two Leopard Imaging LI-OV580 stereo cameras and a pico-projector to create a 3D wound model. The system calculates the wound area by processing stereoscopic images, utilizing structured-from-motion techniques and convolutional neural networks (CNNs). Additionally, the device measures epipolar and projection errors (PE) during AR-based wound evaluation. A Self-Organizing Map (SOM) was employed to refine the 3D reconstruction of the wound. Results: The AR-PE analysis demonstrated the device's accuracy, with the lowest PE recorded at 20 cm from the wound area. The projection error increased as the measurement distance moved toward the edges of the wound. The average epipolar error (EE) was 0.46 pixels, indicating high precision in stereoscopic measurements. Conclusion: The study introduced a non-invasive CW evaluation device that improves wound assessment accuracy and clinician-patient interaction. By utilizing 3D wound modeling and AR, the system enables more effective communication and progress tracking during WH. The results suggest this technology could enhance CW management, offering a reliable alternative to traditional methods.

Extraction of Iris Characteristics Using a Combination Transform for Biometric Any individual Identification

The iris of the eye is used as an identifying and affirmative biometric in many vital applications such as banking and airports.And iris identification is one of the most reliable and accurate biometric identification systems available due to its stability and lack of sensitivity to longevity. In this article, authorized standard processes such as iris segmentation, normalization, and feature extraction were utilized to examine the iris of the eye. The circular hough transform is used for iris segmentation, while the daughman rubber-sheet model is used for iris picture normalization. Using a combination transform consisting of four consecutive transforms, feature encoding was employed to restore the most distinguishing aspects of irises. The 2-D fast fourier transform (FFT), radon transform, 1-D inverse fast fourier transform (IFFT), and 1-D discrete multi-wavelet transform are all examples of transforms. For pattern clustering, the kohonen self-organizing feature map (SOFM) was employe to the group that distinguished information vectors of the iris image features extracted using the combination transforms in order to visualize the close proximity of comparable irises features and scatter dissimilar ones. The matching process is carried out between test iris images and target iris photos of 224 people from the IITD database, with the similarity measured using the euclidian distance metric A part investigates the change of the parameters: FRR, FAR, TSR) of different collections of the PID and POD with the threshold levels (90:10,80:20,70:30,60:40,50:50) that resulted EER for the various thresholds are 0.070,0.070,0.080,0.090 and 0.095 respectively. The suggested approach yielded a TSR of 98% when compared to conventional methods.

Projected Changes in Rainfall Extremes over West African Cities Under Specific Global Warming Levels Using CORDEX and NEX-GDDP Datasets

We analyze long-term extreme events in daily precipitation for the period of 1950–2005 and future projections from 2006 to 2100 based on representative concentration pathways (RCP8.5) over West Africa, including twelve major cities of the subregion. The study uses two datasets from the Coordinated Regional Downscaling Experiments (CORDEX) and NASA Earth eXchange Global Daily Downscaled Projections (NEX–GDDP), for their potential to simulate extreme rainfall characteristics over West Africa. CORDEX datasets are dynamically downscaled from 9 General Circulation Model (GCM) and the NEX-GDDP is statistically downscaled from 21 GCM simulations from CMIP5. The ensemble members of each dataset were used for the analysis and evaluated with eight observations. The projected changes in the rainfall indices were analyzed at four Global Warming Levels (GWLs) under the RCP8.5 climate-forcing scenario. The projections from the CORDEX and NEX-GDDP ensembles were compared. Self-Organizing Map (SOM) analysis was used to classify the projected changes from the simulations into twelve groups, based on their characteristics. Both CORDEX and NEX-GDDP models perform well in simulating the frequency and intensities of daily rainfall characteristics for important parts of the domain. However, substantial differences exist between the models and their relative performance varies with the indices. In all cases, NEX-GDDP outperforms CORDEX in stimulating the extreme indices. On the projections, CORDEX showed a wider spread than the NEX–GDDP for RTOT and vice versa for R97.5pTOT for all the GWLs.

An innovative patient clustering method using data envelopment Analysis–Discriminant analysis and artificial neural networks: A case study in healthcare systems

A major lesson healthcare managers learned from the COVID-19 outbreak is the need for more effective patient classification and medical resource allocation for future pandemics. In their view, hospitalization mortality could be greatly reduced if more effective systems for patient classification were in place before the outbreak to evaluate and assign treatment facilities. This study presents a scalable patient clustering approach using a Self-Organizing Map (SOM) of the Artificial Neural Network (ANN) to cluster patients for appropriate treatment allocation. The patients’ membership is forecasted using Data Envelopment Analysis–Discriminant Analysis (DEA-DA). The objectives of this research are to develop a flexible framework that healthcare systems can adopt to cluster patients based on specific testing criteria from medical records and to assign them to suitable medical centers with appropriate treatment resources. This method aims to enhance healthcare system efficiency by ensuring patients with severe illnesses receive care at well-equipped centers, while those with milder symptoms are directed to other suitable facilities. The approach is scalable and adaptable to any type of widespread illness and aims to increase recovery rates and decrease mortality rates, as confirmed by the case study results.

Impact of stellar population synthesis choices on forward modelling-based redshift distribution estimates

Context. The forward modelling of galaxy surveys has recently gathered interest as one of the primary methods to achieve the required precision on the estimate of the redshift distributions for stage IV surveys, allowing them to perform cosmological tests with unprecedented accuracy. One of the key aspects of forward modelling a galaxy survey is the connection between the physical properties drawn from a galaxy population model and the intrinsic galaxy spectral energy distributions (SEDs), achieved through stellar population synthesis (SPS) codes (e.g. FSPS). However, SPS requires a large number of detailed assumptions on the constituents of galaxies, for which the model choice or parameter values are currently uncertain. Aims. In this work, we perform a sensitivity study of the impact that the variations of the SED modelling choices have on the mean and scatter of the tomographic galaxy redshift distributions. Methods. We assumed the PROSPECTOR-β model as the fiducial input galaxy population model and used its SPS parameters to build 9-bands ugriZYJHKs observed-frame magnitudes of a fiducial sample of galaxies. We then built samples of galaxy magnitudes by varying one SED modelling choice at a time. We modelled the colour-redshift relation of these galaxy samples using the self-organising map (SOM) approach that optimally groups similar redshifts galaxies by their multidimensional colours. We placed galaxies in the SOM cells according to their simulated observed-frame colours and used their cell assignment to build colour-selected tomographic bins. Finally, we compared each variant’s binned redshift distributions against the estimates obtained for the original PROSPECTOR-β model. Results. We find that the SED components related to the initial mass function, as well as the active galactic nuclei, the gas physics, and the attenuation law substantially bias the mean and the scatter of the tomographic redshift distributions with respect to those estimated with the fiducial model. Conclusions. For the uncertainty of these choices currently present in the literature and regardless of the applied stellar mass function based re-weighting strategy, the bias in the mean and the scatter of the tomographic redshift distributions are greater than the precision requirements set by next-generation Stage IV galaxy surveys, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and Euclid.

Optimization and knowledge discovery of profiled end walls in a turbine stage at a low Reynolds number

To comprehensively explore flow control method of profiled end wall for turbine stage at low Reynolds numbers, a surrogate model optimization platform including non-uniform rational B-spline surface parameterization method, support vector regression, and improved chaos particle swarm optimization algorithm is integrated. Optimization designs have been carried out for stator profiled end walls, rotor profiled end wall, and combined end walls, respectively. The results indicate that under the constraint of the output power, the application of various profiled end wall design cases all can effectively improve the aerodynamic performance of the turbine stage. By organizing the flow field of downstream rotor, the profiled end wall of stator can significantly affect the stage efficiency. The flow control benefits of the profiled end wall of the rotor is from the obstruction of the cross migration of the pressure side leg of the horseshoe vortex. The application of profiled end wall on stator has the most practical engineering value. Self-organizing maps and Shapley methods are used to explore potential correlation information of aerodynamic parameters and summarize design experience. The sensitive design variables of profiled end walls are extracted. Based on the local controllability of NURBS surfaces, the regions that affect the stage efficiency are mainly concentrated in the middle of the stator passage, near the stator trailing edge and near the rotor leading edge. The regions with a significant impact on the output power of the turbine stage are near the trailing edge of the rotor and stator. The corresponding design rules of end walls modeling are summarized.

Impact of Skin Care Products on Phthalates and Phthalate Replacements in Children: the ECHO-FGS.

Phthalates and their replacements have been implicated as developmental toxicants. Young children may be exposed to phthalates/replacements when using skin care products (SCPs). Our objective is to assess the associations between use of SCPs and children's urinary phthalate/replacement metabolite concentrations. Children (4-8 years old) from the Environmental Influences on Child Health Outcomes-Fetal Growth Study (ECHO-FGS) cohort provided spot urine samples from 2017 to 2019, and mothers were queried about children's SCP use in the past 24 h (). Concentrations of 16 urinary phthalate/replacement metabolites were determined by liquid chromatography-tandem mass spectrometry (). We used linear regression to estimate the child's use of different SCPs as individual predictors of urinary phthalate/replacement metabolites, adjusted for urinary specific gravity, age, sex assigned at birth, body mass index, and self-reported race/ethnic identity, as well as maternal education, and season of specimen collection. We created self-organizing maps (SOM) to group children into "exposure profiles" that reflect discovered patterns of use for multiple SCPs. Children had lotions applied (43.0%) frequently, but "2-in-1" hair-care products (7.5%), sunscreens (5.9%), and oils (4.3%) infrequently. Use of lotions was associated with 1.17-fold [95% confidence interval (CI): 1.00, 1.34] greater mono-benzyl phthalate and oils with 2.86-fold (95% CI: 1.89, 4.31) greater monoethyl phthalate (MEP), 1.43-fold (95% CI: 1.09, 1.90) greater monobutyl phthalate (MBP), and 1.40-fold (95% CI: 1.22, 1.61) greater low-molecular-weight phthalates (LMW). Use of 2-in-1 haircare products was associated with 0.84-fold (95% CI: 0.72, 0.97) and 0.78-fold (95% CI: 0.62, 0.98) lesser mono(3-carboxypropyl) phthalate (MCPP) and MBP, respectively. Child's race/ethnic identity modified the associations of lotions with LMW, oils with MEP and LMW, sunscreen with MCPP, ointments with MEP, and hair conditioner with MCPP. SOM identified four distinct SCP-use exposure scenarios (i.e., profiles) within our population that predicted 1.09-fold (95% CI: 1.03, 1.15) greater mono-carboxy isononyl phthalate, 1.31-fold (95% CI: 0.98, 1.77) greater mono-2-ethyl-5-hydroxyhexyl terephthalate, 1.13-fold (95% CI: 0.99, 1.29) greater monoethylhexyl phthalate, and 1.04-fold (95% CI: 1.00, 1.09) greater diethylhexyl phthalate. We found that reported SCP use was associated with urinary phthalate/replacement metabolites in young children. These results may inform policymakers, clinicians, and parents to help limit children's exposure to developmental toxicants. https://doi.org/10.1289/EHP13937.

Toward mainstreaming care activities in transportation: a time use and mobility segmentation approach

ABSTRACT This paper assesses the importance of incorporating care dimensions into activity-travel segmentation to understand daily life mobility strategies. The data came from six neighbourhoods in Concepción, Chile, and included detailed information on activity-travel time use and interaction and classification schemes to identify care purposes. The study uses self-organizing maps to build incremental behavioural segments from weekly mobility and time use variables, adding care activities to assess their role in this segmentation. The results identify groups with a higher burden on care than others, emphasizing the role of transport mode and time use patterns. The result remarks caregiving activities hidden within other categories, identifying groups of caregivers, including domestic workers and women who work and have intense accompanying activities with children. The results highlight the differences between mobility patterns between different segments to make more invisible care and other related activities disproportionately performed by groups of women.

From Seeing to Recognising–An Extended Self-Organizing Map for Human Postures Identification

From Seeing to Recognising–An Extended Self-Organizing Map for Human Postures Identification

Application of Deep Learning for Reservoir Porosity Prediction and Self Organizing Map for Lithofacies Prediction

While there is a connection between petrophysical logs and reservoir porosity, finding analytical solutions for this relationship is still difficult. This paper presents a novel approach for forecasting porosity and lithofacies by using a convolutional neural network (CNN) model in conjunction with a bi-directional long short-term memory (BLSTM) network. The BLSTM network uses a self-organizing map (SOM) technique to form connections between input and destination data. The SOM is used to organize depth intervals with similar facies into four separate clusters, each exhibiting internal consistency in petrophysical parameters. The CNN is responsible for extracting spatial characteristics, while the BLSTM network gathers comprehensive spatiotemporal components, guaranteeing that the model accurately represents the spatiotemporal aspects of log data. The accuracy of the model was verified by analyzing simulation logging data. The findings indicate that the BLSTM network model successfully recovers significant characteristics from logging data, resulting in improved estimate accuracy. In addition, Facies-01 has lower gamma ray levels in comparison to other facies. Facies-01 is also suggestive of pristine sandstone formations, which are greatly sought as reservoir rocks. The BLSTM network model is effective in predicting physical characteristics of reservoirs, offering a new method for precise reservoir characterization parameter prediction.

Evaluation of Antipsychotic-Induced Neuroleptic Malignant Syndrome Using a Self-Organizing Map.

Introduction In neuropsychiatric pharmacotherapy, neuroleptic malignant syndrome (NMS) is a potentially serious side effect of antipsychotics characterized primarily by fever, disorientation, extrapyramidal disorders, and autonomic nervous system imbalance, which can lead to death if left untreated. We visualized the NMS profile of antipsychotics using a self-organizing map (SOM). We combined it with decision tree analysis to discriminate between 31 antipsychotics in more detail than typical antipsychotic (TAP) and atypical antipsychotic (AAP) classifications. Method A total of 20 TAPs and 11 AAPs were analyzed. We analyzed NMS reports extracted from the Japanese Adverse Drug Event Report (JADER) database based on standardized Medical Dictionary for Regulatory Activities (MedDRA) queries (Standardized MedDRA Queries (SMQ) code: 20000044, including 68 preferred terms). The SOM was applied using the SOM package in R version 4.1.2 (RFoundation for Statistical Computing, Vienna, Austria). Results The Japanese Adverse Drug Event Report (JADER) database contained 887,704 reports published between April 2004 and March 2024. The numbers of cases of NMS (SMQ code: 20000044) reported for risperidone, aripiprazole, haloperidol, olanzapine, and quetiapine were 1691, 1294, 1132, 1056, and 986, respectively. After the antipsychotics were classified into six units using SOM, they were adapted for decision tree analysis. First, 31 antipsychotics branched off into groups with loss of consciousness, with one group (10 TAPs) consisting entirely of TAPs, and the other consisting of antipsychotics that were further separated into two groups with coma induced by TAPs and AAPs. Conclusion The results of this study provide a reference for healthcare providers when predicting the NMS characteristics induced by each drug in patients, thereby facilitating the effective treatment of schizophrenia.

Reducing Cluster Overlap in Movie Recommendations with IKSOM and Silhouette Clustering

The exponential growth of online material requires the implementation of effective and precise recommendation systems in order to optimize the user experience. Nevertheless, conventional approaches frequently encounter problems such as cluster overlap, which reduces the accuracy of suggestions. This paper presents a new method for minimising the overlap between clusters in movie recommendation systems. It achieves this by combining Improved Kohonen Self-Organizing Maps (IKSOM) with Silhouette Clustering. The proposed method utilises IKSOM to efficiently represent high-dimensional user-item interactions in a two-dimensional space, enabling the formation of distinct and meaningful clusters. Subsequently, Silhouette Clustering is utilised to enhance the separation and cohesion of clusters, hence reducing overlap. The experimental findings show that proposed hybrid model works much better than the baseline techniques, obtaining an RMSE of 0.423 and MAE of 0.216. Additionally, it improves precision (93.6%), recall (94.2%) and F1-score (93.4%). Additionally, the proposed technique demonstrates a high level of accuracy (97.3%) with a precision rate of 95.8%. These results emphasise the method's efficacy in minimising errors and enhancing the overall performance of the recommendation system. The results indicate that combining IKSOM with Silhouette Clustering can improve the precision and dependability of movie recommendation systems by resolving cluster overlap and offering more individualised user experiences. Subsequent research will investigate the implementation of this method in different fields and the integration of supplementary contextual information to enhance the accuracy of recommendations.

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.

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics.

The contribution of mica mining activities to fluoride (F-) contamination in groundwater has been chased in this study. For the purpose, groundwater samples (n = 40, replicated thrice) were collected during the post-monsoons (September-October) from a mica mining area in the Tisri block of Giridih district, Jharkhand. The study has employed a synergy of classical aquifer chemistry, statistical approaches, different indices, Self-Organising Maps (SOM), and Sobol sensitivity index (SSI) to unveil the underlying aquifer chemistry, identify the impacts of mining activities on groundwater quality and its associated health hazard. Fluoride levels varied from 0.34 to 2.8ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5ppm). Physicochemical analysis revealed significant differences in electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and major ion concentrations (Na+, HCO3-, Ca2+) between fluoride-contaminated (FC) and fluoride-uncontaminated (FU) groups. Higher Na+ and HCO3- associated with F- contaminated samples, were indicative of silicate weathering and carbonate dissolution as primary geogenic sources for this ion. Health risk assessment (HRA) revealed hazard quotient (HQ) values exceeding unity, indicating non-carcinogenic risks, particularly for children in most samples from group FC. The mean Water Quality Index (WQI) of FC group (156.76 ± 7.30) was significantly higher (p < 0.05) than group FU indicating of its unsuitability. SOM could accurately (80%) predict presence of fluoride in water samples based on other major ions. Sobol sensitivity analysis successfully identified fluoride concentration and body weight as most impactful parameters affecting human health. The integration of advanced modelling techniques and geospatial analysis as Inverse Distance Weightage (IDW) maps has provided a robust framework for ongoing groundwater quality monitoring in mining-affected regions and can help proactive intervention in risk-prone areas. Overall, this comprehensive study takes us a step ahead towards ensuring safe drinking water access for the global community.

Determining the onset of summer rainfall over Vietnam using self-organizing maps

Determining the onset of summer rainfall over Vietnam using self-organizing maps

Revealing the distribution characteristics and key driving factors of dissolved organic matter in Baiyangdian Lake inflow rivers from different seasons and sources

The river course is a transitional area connecting the source and receiving water bodies. The dissolved organic matter (DOM) in the river course is an important factor affecting the aquatic environment and ecological health. However, there are shortcomings in studying the differences and quantitative contributions of river DOM in different seasons and sources. In this study, ultraviolet-visible (UV–vis) and three-dimensional fluorescence spectra were used to characterize the optical properties, analyze the spatiotemporal changes, and establish the quantitative relationship between environmental factors and DOM in the inflow rivers of Baiyangdian Lake. The results showed that the relative DOM concentrations in summer and autumn were significantly higher than those in the other seasons (P < 0.001) and that the DOM source (SR < 1) was mainly exogenous. The fluorescence abundance of protein-like substances (C1 + C2 + C3) was the highest in spring, whereas that of humus C4 was the highest in autumn. Moreover, the inflow rivers exhibited strong autogenetic characteristics (BIX > 1) throughout the year. Self-organizing maps (SOM) indicated that the main driving factors of water quality were NO3−-N in spring, autumn, and winter and DO, pH, and chemical oxygen demand (COD) in summer. Random forest analysis showed that the fluorescent components (C1–C4) were closely related to the migration and transformation of nitrogen, and pH and nitrogen were the main predictors of each component. The Mantel test and structural equation model (SEM) showed that temperature and NO3−-N significantly influenced the DOM concentration, components, and molecular properties in different seasons. Moreover, the river source also affected the distribution mechanism of DOM in the water body. Our study comprehensively analyzed the response of DOM in inflow rivers in different seasons and water sources, providing a basis for further understanding the driving mechanisms of water quality.

Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration

The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities.

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks

The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the landfill needs more precise management. To solve this problem, based on the “pressure-state-response” framework, a method for the identification and evaluation of groundwater pollution around the landfill was constructed. The LPI method was used to assess the contamination potential of the leachate. The comprehensive quality of groundwater was evaluated by the entropy-AHP water quality assessment method, sodium adsorption ratio and sodium percentage. The probabilistic health risks of groundwater were assessed based on a Monte Carlo algorithm. The sources of pollutants were identified by comprehensively using the PCA-APCS-MLR model and the PMF model. Finally, the self-organizing map algorithm and the Kmeans algorithm were integrated to enhance the precision of groundwater management and control measures. The results showed that the leachate of the landfill was in the mature stage, and the concentration of inorganic substances was relatively high. Leachate had the potential to contaminate surrounding groundwater. The groundwater quality of 68.14% of the study area was in the poor or lower level. The groundwater near the landfill was unsuitable not only for drinking but also for irrigation purposes. Cl− was the main non-carcinogenic risk factor. Reducing pollutant concentration and controlling exposure time are effective strategies for mitigating health risks caused by high-concentration pollutants (Cl−, NO3−) and low-concentration pollutants (F−), respectively. The groundwater around the landfill was jointly affected by six pollution sources. The PMF model has better analytical ability in mixed pollution areas. The groundwater in the study area was divided into five clusters, of which cluster Ⅰ was significantly affected by leachate, and cluster Ⅴ had the lowest pollution and health risk.