Self-organizing Map Method Research Articles

Measuring and Mapping Physical Activity Disparity (PAD) Index Based on Physical Activity Environment for Children

Physical activity (PA) plays a vital role in children’s physical and mental health. The built, natural, and socio-demographic environmental variables affect children’s PA behaviors in various ways. However, few studies focus on systematically measuring the environmental spatiality to enhance PA research. We propose a Physical activity Access Disparity (PAD) index for children. This study aims to design, test, and apply an integrated approach to the children’s PAD index. We adopt five dimensions of “access” to healthcare to measure the children’s PAD index for the United States (US) and the state of Georgia at the county level. The PAD index sorts 18 environmental measures with 23 variables into accessibility, availability, accommodation, affordability, and acceptability (5 As) for children’s PA. We use the self-organizing map (SOM) method to measure how the 5 As affect the PAD index values. According to the result, the children’s PAD index’s ranking normalizes from 0 to 1 and identifies “play oases” to “play deserts” in the US and Georgia using diverse 5 As combinations. The children’s PAD index shows Low disparity in the north and coastal region and High disparity in Deep South states in the US. Moreover, the PAD index shows Low disparity and High disparity in the north and south of Georgia. The PAD index provides a valuable tool for researchers and policymakers to analyze disparity in children’s “access” to the PA environment. The flexible parameters and the weighing scheme also extend the method’s generality and allow users to customize the PAD index based on local preferences and conditions.

Self-Organized Neural Network Method to Identify Crash Hotspots

Crash hotspot identification (HSID) is an essential component of traffic management authorities’ efforts to improve safety and allocate limited resources. This paper presents a method for identifying hotspots using self-organizing maps (SOM). The SOM method was used to identify high-risk areas based on five commonly used HSID methods: crash frequency, equivalent property damage only, crash rate, empirical Bayes, and the societal risk-based method. Crashes on a major road in Iran were examined using the proposed method. Based on these criteria, high-risk locations were grouped into six clusters, which provided appropriate criteria for each location depending on the importance of the cluster. The findings show that the identification of crash hotspots tends to focus on areas with more crashes and deaths, demonstrating that the research methodology was appropriate.

A Novel Three-Stage Optimization Design Method of Asymmetric-PM Variable Flux Memory Machine Considering Magnet-Axis-Shifting Effect

Asymmetric-permanent-magnet variable flux memory machine (APM-VFMM) commonly suffers from the optimization issues, i.e., variable magnet-axis-shifting (MAS) effects result in distinctly differentiated electromagnetic characteristics under multiple magnetization states (MSs). Thus, this article proposes a novel three-stage optimization method for APM-VFMM considering the MAS effects to achieve efficient optimization. In the first stage, an analytical model based on the magnetic equivalent circuit (MEC) and torque models considering the MAS effects is developed to determine the key optimization objectives of the investigated machine while theoretically revealing the internal mechanisms for the improvement of the flux regulation range and torque capability. In the second stage, the comprehensive sensitivity analyses of the main geometric parameters are implemented based on a self-organizing map (SOM) method. Subsequently, the geometric parameters with higher sensitivity are selected for the multiobjective optimization process, thus effectively reducing the computational burden. In the third stage, a multimode circuit control model is developed and utilized in a single finite element analysis (FEA) case to consider multiple MSs and different MAS effects simultaneously for multiobjective optimization. The FEA predictions and experimental tests carried out on an APM-VFMM prototype confirm the effectiveness of the proposed three-stage optimization method.

Aerodynamic and structural multidisciplinary optimization design method of fan rotors based on blade curvature constraints

The optimization design of the actual fan/compressor rotors is a multidisciplinary problem. The aerodynamics and strength multidisciplinary optimization design of the fan/compressor rotors can improve the aerodynamic performance of rotors as much as possible under the condition of satisfying the structural strength requirements. However, the conventional multidisciplinary optimization of aerodynamics and structures will significantly increase the amount of computation cost and time period. Thus, it is difficult for conventional multidisciplinary optimization methods to be used in real-world engineering practices. In order to reduce the computational burden and time cost of the multidisciplinary optimization design, this paper firstly proposes a multidisciplinary optimization design method for rotors based on blade curvature constraints. In the optimization, the self-organizing map (SOM) method is applied to analyze and extract the constraint value of the blade curvature. And the blade curvature constraint value is used as a penalty function instead of the time-consuming high-fidelity FEM, which greatly reduces the computational cost and runtime of the multidisciplinary optimization. Besides, the Free-form deformation (FFD) method is adopted to realize the flexible deformation of the three-dimensional blade with the smallest number of design variables and polynomial chaos Kriging is used as a surrogate model for aerodynamic performance prediction in the optimization process. The method verification and mechanism analysis are carried out by studying a fan rotor. Data mining of FEM samples shows that, in the optimization design space, the maximum spanwise curvature of the blade is monotonically related to the maximum stress of the blade. And the maximum streamwise curvature, maximum spanwise slope, maximum streamwise slope, and maximum thickness of the blade have no related relationship with the maximum stress of the blade. Compared with the conventional multidisciplinary optimization design method based on the surrogate models of high-fidelity computational fluid dynamics (CFD) and finite element method (FEM), the time cost of the multidisciplinary optimization design method based on curvature constraints is reduced by 10 times, and the time cost of the multidisciplinary optimization is significantly reduced. The results show that, in terms of structural performance, the maximum stress of the aerodynamic optimization method without blade curvature constraints is 426 MPa, which exceeds the yield limit of aluminum alloy material (420 MPa), and the maximum stress of the multidisciplinary optimization design with blade curvature constraints proposed in this paper is 344 MPa. In terms of aerodynamic performance, the isentropic efficiency of the aerodynamic optimization method without blade curvature constraints and the method proposed in this paper are increased by 2.1% and 1.8%, respectively. Therefore, the proposed method reduces the maximum stress of the blade by 19.5% with little influence on the aerodynamic performance.

Visualization and analysis of Pareto-optimal fronts using interpretable self-organizing map (iSOM)

Visualizing and analyzing multiple Pareto-optimal solutions obtained using an evolutionary multi- or many-objective optimization algorithm is as important a task as the task of finding them. Besides helping to choose a single preferred solution, they provide a better understanding of the trade-off among the objectives and also reveal key insights about interactions among variables and objectives for the Pareto-optimal solutions. Existing visualization methods do not provide a comprehensive account of both visualization and analysis of Pareto-optimal solutions. In this paper, we present an interpretable self-organizing map (iSOM) method that produces a more simplistic mapping of higher-dimensional variable spaces into two dimensions. Multiple iSOM plots, one for each objective, allows an easier visual understanding of trade-off among objectives. By identifying high trade-off Pareto-optimal solutions and marking them on the iSOM plots, we also provide decision-makers a comprehensive method to locate critical and likely preferred solutions on the Pareto-optimal front. The visualization and analysis of Pareto-optimal solutions using iSOMs are demonstrated on 11 problems involving three to five objectives. As discussed, the approach is generically applicable to higher-dimensional and constrained problems.

Identifying Traffic Congestion Patterns of Urban Road Network Based on Traffic Performance Index

Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

Objective circulation classification of rainstorm days associated with Northeast China cold vortexes in the warm seasons of 2000–19

This study conducts objective circulation classifications of rainstorm days associated with Northeast China Cold Vortexes (NECVs) in the northeast of China (NEC) during the warm seasons (May–September). To determine the optimal method and number of types, the performances of ten objective circulation classification methods are first evaluated by several evaluation indexes. Self-Organizing Maps method is then used as the optimal method to classify rainstorms into five types. The results show that the different synoptic circulation patterns are accompanied by distinctive large-scale circulation backgrounds, precipitation characteristics, thermodynamic and moisture conditions. In type 1, the strong western Pacific subtropical high extends north to connect with the mid-latitude ridge in the east of the NEC, and a shallow trough lies in the west of the NEC. This configuration brings the most daily and hourly mean precipitation of all types. A low-pressure anomaly with an obvious trough controls the NEC in type 2, which has a higher frequency. In type 3, the low-pressure anomaly shrinks to the south of the NEC, and the NEC is controlled by the cut-off low vortex. Type 4 has the strongest hourly precipitation and features a meridional high-low-high pressure anomaly, and the narrow zonal low-pressure anomaly is in the NEC. Two low-pressure anomalies and a westerly trough can be found in type 5 and are distributed in a southwest-northeast orientation. These synoptic circulation patterns and the corresponding spatial distribution of rainstorm-day precipitation indicate that the objective circulation classification is effective in helping understand the large-scale circulation and precipitation characteristics associated with NECVs.

Machine learning based on the graph convolutional self-organizing map method increases the accuracy of pollution source identification: A case study of trace metal(loid)s in soils of Jiangmen City, south China

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

Ozone (O3) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O3 concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O3 model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O3 concentration can be accurately estimated; the R2 (RMSE: μg/m3) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O3 concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O3 concentration had increased significantly. (3) The O3 concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.

Assessing public opinion using self-organizing maps. Lessons from urban planning in Romania

Public consultation is an essential component of the planning process to achieve sustainable development. However, there is still a lack of knowledge related to public engagement in consultation processes on urban planning matters. Thus, the aim of this study was to understand public needs in terms of planning urban space by exploring the content of online comments made by the public. We used, as a case study, urban planning in Bucharest, Romania. We assessed 1879 online comments submitted between 2008 and 2020 through a municipality-set online platform, to 220 out of the 1148 zonal urban plans issued during that period. The self-organizing map (SOM) method, an unsupervised artificial neural network, was used to visualize and cluster the online comments based on their content. Using this method, 13 clusters were found to show the general landscape of topics in online comments. These revealed three main and frequently discussed issues among the public: urban green spaces (i.e., saving or developing new green spaces), protection of cultural heritage (i.e., preserving the cultural heritage of the city) and administrative issues (i.e., lack of confidence in the decision-making and permit granting process). In terms of dynamics over the analyzed time period, the topics evolved from esthetic and cultural concerns to issues relating to administrative shortcomings and lack of urban green spaces. Furthermore, we investigated the relationship between plan characteristics and comments and found that the objective of the plan and its year of approval have a positive influence on the presence and number of comments. These results are important to produce higher quality urban projects that are better aligned with the public needs. We further suggest the need to support a sustainable urban spatial development by incorporating the real public needs.

Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Digital Twin.

Bearing is a key part of rotating machinery. Accurate prediction of bearing life can avoid serious failures. To address the current problem of low accuracy and poor predictability of bearing life prediction, a bearing life prediction method based on digital twins is proposed. Firstly, the vibration signals of rolling bearings are collected, and the time-domain and frequency-domain features of the actual data set are extracted to construct the feature matrix. Then unsupervised classification and feature selection are carried out by improving the self-organizing feature mapping method. Using sensitive features to construct a twin dataset framework and using the integrated learning CatBoost method to supplement the missing data sets, a complete digital twin dataset is formed. Secondly, important information is extracted through macro and micro attention mechanisms to achieve weight amplification. The life prediction of rolling bearing is realized by using fusion features. Finally, the proposed method is verified by experiments. The experimental results show that this method can predict the bearing life with a limited amount of measured data, which is superior to other prediction methods and can provide a new idea for the health prediction and management of mechanical components.

A hybrid method for day‐ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder

Photovoltaic (PV) generation has high impact on the decarbonization pathways of power systems. Accuracy of day-ahead PV power forecasting has become crucial in the operation and control of power system with high PV penetration. This paper develops a hybrid approach based on generative adversarial network (GAN) combined with convolutional autoencoder (CAE) to improve PV power forecasting accuracy. Self-organizing map method is first utilized as data pre-processing to classify target days into different weather types based on solar irradiance. With the ability of GAN to reduce the burden of loss and the advantages of CAE to extract multi-scale effective features from the weather and PV power, PV power forecasting model consisting of GAN and CAE is proposed. The developed method has been tested on a real dataset in a Chinese PV station and compared with base reference PV forecasting methods. Numerical testing results demonstrate the effectiveness of our method with high accuracy.

Features and Evolution of Autumn Weather Regimes in the Southeast China

Autumn is the transitional season when the atmospheric circulation pattern changes from summer to winter. The temperature and precipitation in Southeastern China in autumn are significantly influenced by the change in circulation patterns, and both show significant uniqueness. The clustering method can be used to observe the changes of circulation patterns in detail and to observe and analyze the transition from warm to cold seasons from a detailed view of the daily circulation pattern perspective. This method may have important research implications on how to study the generation and dissipation of extreme weather events. The Self-Organizing Maps (SOM) method is used to a 500 hPa geopotential height and 850 hPa wind and sea level pressure for 1981–2020 to identify the characteristic weather patterns (WTs) in autumn (September–November) over Southeastern China. Characteristics of the captured WTs are also analyzed in terms of the distribution characteristics of weather patterns, occurrence frequency, typical progression, precipitation and extreme precipitation (EP), temperature and extreme high temperature (EHT), and the relationship with atmospheric teleconnection. Nine WTs were identified in autumn, which represents a series of weather situations consisting of troughs and ridges. On this basis, these WTs were used to carry out the differentiation of seasonal differences between early and late autumn. The maximum mean and extreme precipitation occur in several early season patterns (WT1, WT2, WT4, and WT7). It is highly likely that extremely high temperatures occur in the WT1 and WT2 patterns. The most common progression between WTs is WT7−WT1−WT2−WT4 in the early season. This seasonality allows us to distinguish between early and late seasons based on daily weather types. A preliminary trend analysis suggests that patterns in the early season occur more frequently and last longer in the early season, and patterns in the late season occur less frequently and later. That is, the longer cool season pattern is shifting to the shorter warm season pattern. In addition, the persistence of both cool and warm patterns increased during 2001–2020 relative to 1981–2000, and the risk of both flooding and drought occurrence is on the rise.

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling.

The Meyal oil field (MOF) is among the most important contributors to Pakistan's oil and gas industry. Northern Pakistan's Potwar Basin is located in the foreland and thrust bands of the Himalayan mountains. The current research aims to delineate the hydrocarbon potential, reservoir zone evaluation, and lithofacies identification through the utilization of seven conventional well logs (M-01, M-08, M-10, M-12, M-13P, and M-17). We employed the advanced unsupervised machine-learning method of self-organizing maps for lithofacies identification and the novel Quanti Elan model technique for comprehensive multimineral evaluation. The shale volume, porosity, permeability, and water saturation (petrophysical parameters) of six wells were evaluated to identify the reservoir potential and prospective reservoir zones. Well-logging data and self-organizing maps were used in this study to provide a less costly method for the objective and systematic identification of lithofacies. According to the SOM and Pickett plot analyses, the zone of interest is mostly made up of pure limestone oil zone, whereas the sandy and dolomitic behavior with a mixture of shale content shows non-reservoir oil-water and water zones. The reservoir has good porosity values that range from 0 to 18%, but there is a high water saturation of up to 45% in reservoir production zones. The presence of shale in the entire reservoir interval has a negative effect on the permeability value, but the petrophysical properties of the Meyal oil reservoir are good enough to permit hydrocarbon production. According to the petrophysical estimates, the Meyal oil field's Sakesar and Chorgali Formations are promising reservoirs, and new prospects for drilling wells in the southern and central portions of the eastern portion of the research area are recommended.

Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm

Betel leaf is one of the plants that is widely used as a natural or traditional medicine by the community, natural treatment with the use of plants is relatively safer. But there is a problem when we choose healthy betel leaves because of our mistakes in choosing which betel leaves are healthy and which are not. With this research the authors aim to detect healthy and sick betel leaves using data collection. Feature extraction used is the value of Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV) to get the characteristics of the color image. Then the results of the feature extraction are used to classify the health of green betel leaves using the Self-Organizing Maps method. The green betel leaf data used is 1500 images for train data and 450 images for testing data are image test data, test data that produces an evaluation value with an accuracy value of 97.20% on the Self-Organizing Maps method.

Performance Evaluation of Wireless Sensor Networks' Communication Overhead and Energy Consumption using the Self-Organizing Map Method

Performance Evaluation of Wireless Sensor Networks' Communication Overhead and Energy Consumption using the Self-Organizing Map Method

Effects of Wettability and Minerals on Residual Oil Distributions Based on Digital Rock and Machine Learning

Abstract The wettability of mineral surfaces has significant impacts on transport mechanisms of two-phase flow, distribution characteristics of fluids, and the formation mechanisms of residual oil during water flooding. However, few studies have investigated such effects of mineral type and its surface wettability on rock properties in the literature. To unravel the dependence of hydrodynamics on wettability and minerals distribution, we designed a new experimental procedure that combined the multiphase flow experiments with a CT scan and QEMSCAN to obtain 3D digital models with multiple minerals and fluids. With the aid of QEMSCAN, six mineral components and two fluids in sandstones were segmented from the CT data based on the histogram threshold and watershed methods. Then, a mineral surface analysis algorithm was proposed to extract the mineral surface and classify its mineral categories. The in situ contact angle and pore occupancy were calculated to reveal the wettability variation of mineral surface and distribution characteristics of fluids. According to the shape features of the oil phase, the self-organizing map (SOM) method, one of the machine learning methods, was used to classify the residual oil into five types, namely, network, cluster, film, isolated, and droplet oil. The results indicate that each mineral’s contribution to the mineral surface is not proportional to its relative content. Feldspar, quartz, and clay are the main minerals in the studied sandstones and play a controlling role in the wettability variation. Different wettability samples show various characteristics of pore occupancy. The water flooding front of the weakly water-wet to intermediate-wet sample is uniform, and oil is effectively displaced in all pores with a long oil production period. The water-wet sample demonstrates severe fingering, with a high pore occupancy change rate in large pores and a short oil production period. The residual oil patterns gradually evolve from networks to clusters, isolated, and films due to the effects of snap-off and wettability inversion. This paper reveals the effects of wettability of mineral surface on the distribution characteristics and formation mechanisms of residual oil, which offers us an in-deep understanding of the impacts of wettability and minerals on multiphase flow and helps us make good schemes to improve oil recovery.

Spatial Heterogeneity of CDOM, Optical Brighteners, and Oils in Mesohaline Tidal Creeks Using Self-Organizing Maps

Shallow tidal creek systems or triblets are often overlooked when documenting and measuring the spatial extent of pollutants of emerging concern despite much of the population living in and around these areas. An innovative in situ fluorometric instrument coupled with a Self-Organi21zing Map was utilized in Chesapeake Bay’s mesohaline tidal creek system to analyze CDOM, dissolved oxygen, optical brighteners, and oils. The in situ fluorometer proved helpful as a rapid reconnaissance tool complementing the investigation when attached to a CTD instrument. This baseline research showed that CDOM follows non-conservative properties in spring and more conservative behavior in the fall. The results show that the Self-Organizing Map method is a suitable alternative to traditional statistical techniques and may be better at finding key patterns that might otherwise have been obscured by high variability. For example, oils revealed a pattern with residual runoff from highways or boating, while optical brighteners displayed a pattern consistent with septic systems. Optical brighteners also revealed lag effects after the passing of heavy rainfall and were consistent with the lab effect of turbidity. The study also reveals that CDOM is the dominant control on light penetration, one of the limiting factors on underwater grass growth. The results also suggest that CDOM should not be overlooked when measuring the effects of restoration in these systems and should be implemented in regular monitoring and TMDLs.

Regional Groundwater Water Quality Assessment and Contamination Source Identification by a Self-Organizing Map and Entropy Method in Pinggu Basin, Northeast Beijing

Groundwater quality assessment is important for understanding the suitability of groundwater resources for various purposes. Although many different methods have been proposed for this purpose, few methods have considered the spatial variation of groundwater components during the assessments. In this study, we proposed to combine the self-organizing map (SOM) and entropy-based weight determining method to assess groundwater quality. Totally, 955 water samples taken from 58 wells during 2010–2017 were used in the study. 22 hydrochemical components (K+, Na+, Ca2+, Mg2+, NH4+, Cl−, SO42-, F−, NO3−, Fe2+, Fe3+, Al, etc.) were used in the assessment for each sample. These sampling points can be classified into five clusters, which may be affected by four different sources: landfill sources (cluster 3), industrial and agricultural sources (cluster 5), and domestic sewage discharge sources (clusters 1, 2, and 4). The scores of the water quality of the five clusters that were calculated by the entropy method are 0.2658, 0.2634, 0.5737, 0.2608, and 0.5718, indicating that the groundwater affected by domestic sewage discharge sources (clusters 1, 2, and 4) are better than other two sources (clusters 3 and 5) in the study area. The results of this study provide insights for the protection of groundwater resources and the treatment of groundwater pollution in the future.

Land Use Zoning Management to Coordinate the Supply–Demand Imbalance of Ecosystem Services: A Case Study in the City Belt Along the Yellow River in Ningxia, China

The imbalance between the supply and demand of ecosystem services (ESs) is one of the main reasons for ecological degradation, which significantly impacts human well-being and ecological safety. Spatial heterogeneity of ES supply–demand, ES tradeoffs, and the socioecological environment calls for zoning management, while few studies have combined the above three aspects in dividing management zones and proposed strategies. Using the City Belt along the Yellow River in Ningxia in northwestern China as a case study, this study quantified the supply and demand for five key ESs (crop production, carbon sequestration, nutrient retention, sand fixation, and recreational opportunity), analyzed ES tradeoffs/synergies and bundles through correlation analysis and the self-organizing map (SOM) method, and investigated their socioecological driving mechanisms through a random forest model and the SOM method. Management zones were proposed and differentiated suggestions were provided through overlaying ES bundles and driver clusters. The results suggested that crop production, carbon sequestration, and nutrient retention mostly correlated to the same intrinsic ecological process, resulting in consistent synergies among these three ESs at both supply and demand sides. On the contrary, the variance in interactions between the two ESs of sand fixation and recreational opportunity and the other three ESs is due to the low similarity of their intrinsic ecological processes and external driving mechanisms. Fourteen socioecological factors could effectively explain the spatial heterogeneity of ES supply, demand, and match degree. Fourteen management zones with similar ecological problems and socioecological environments were delineated, and differentiated suggestions were provided for each zone. Adopting both ES characteristics and the socioecological environment into zoning management could effectively detect ecological problems and help to promote management suggestions in different socioecological contexts. This framework could offer new insights for integrating ESs into actual decision-making and ecosystem management.