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Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data. [ABSTRACT FROM AUTHOR]

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability. [ABSTRACT FROM AUTHOR]

Considering the essential expansion of agricultural production, current research primarily focuses on static factors, such as the distribution of fine-grained arable land, omitting an in-depth analysis of its developmental dynamics and key drivers. Addressing this knowledge gap is crucial for enhancing the scalability of agricultural production. This research utilizes landscape ecology techniques, correlation analysis, random forest algorithms, and structural equation modeling to explore spatial pattern trends of arable land in the Beijing–Tianjin–Hebei region. Its objective is to clarify how the expansion of agricultural production scale affects food production through changes in arable land patterns and to determine the impact of socio-economic factors on these configurations. The results show that: (1) the landscape pattern of arable land is transitioning to a more fragmented arrangement with complex contours, (2) grain yield per unit area correlates positively with the landscape pattern index in Beijing, negatively in Hebei, and exhibits no significant correlation in Tianjin, and (3) land ownership plays a crucial role in land fragmentation, alterations in land morphology, and influences other socio-economic variables. Analyzing the spatial pattern of arable land in conjunction with socio-economic factors is essential for developing holistic land management approaches, improving resource efficiency, minimizing external inputs, and mitigating food security challenges. [ABSTRACT FROM AUTHOR]

Global volcanic and plutonic olivines record the compositional characteristics and physicochemical conditions of the parental magmas. Thus, they have significant potential for use as petrogenetic discriminators of the olivine formation environment and prospecting indicators for potential host rocks of magmatic sulfide deposits. Several data visualization approaches have been proposed by researchers to determine olivine origins. However, they can only discriminate specific olivine populations and require the incorporation of trace elements for which data are lacking globally. In this study, a machine-learning method consisting of the random forest algorithm and the synthetic minority oversampling technique (SMOTE) is used to discriminate the crystallization environments of olivine and predict the sulfide potential of olivine-bearing mafic-ultramafic intrusions. We employ a global data set of 24 341 olivine samples from 12 environments to determine the contents of MgO, FeO, Ni, Ca, Mn, and Cr and the Fo number [100 × Mg/(Mg+Fe)]. The results indicate that the proposed method can classify olivine into genetically distinct populations and distinguish olivine derived from mineralized intrusions from that derived from sulfide-barren intrusions with high accuracies (higher than 99% on average). We develop a dimensionality reduction algorithm to visualize the olivine classifications using low-dimensional vectors and an olivine classifier (accessible at http://101.33.204.62:8080/olivine%5fweb/main.html China University of Geosciences, Beijing). The model is used successfully to identify the contributions of distinct sources to regional magmatism using olivines from the late Permian picrite and basalt along the western margin of the Yangtze block (SW China) and to predict the sulfide potential of the newly discovered Qixin mafic-ultramafic complex in the southern Central Asian Orogenic Belt (NW China). The findings suggest that the proposed approach enables the accurate identification of olivine origins in diferent formation environments and is a reliable indicator suitable for global Ni-Cu-platinum group element (PGE) exploration. [ABSTRACT FROM AUTHOR]

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies. [ABSTRACT FROM AUTHOR]

Estimating household water consumption can facilitate infrastructure management and municipal planning. The relatively low explanatory power of household water consumption, although it has been extensively explored based on various techniques and assumptions regarding influencing features, has the potential to be enhanced based on the water-energy nexus concept. This study attempts to explain household water consumption by establishing estimation models, incorporating energy-related features as inputs and providing strong evidence of the need to consider the water-energy nexus to explain water consumption. Traditional statistical (OLS) and machine learning techniques (random forest and XGBoost) are employed using a sample of 1320 households in Beijing, China. The results demonstrate that the inclusion of energy-related features increases the coefficient of determination (R2) by 34.0% on average. XGBoost performs the best among the three techniques. Energy-related features exhibit higher explanatory power and importance than water-related features. These findings provide a feasible modelling basis and can help better understand the household water-energy nexus. [ABSTRACT FROM AUTHOR]

Accurate prediction of PM2.5 concentrations can provide a solid foundation for preventing and controlling air pollution. When the Long Short-Term Memory (LSTM) is applied to predict PM2.5 concentration, the influential factors strongly correlated with PM2.5 concentration are directly fed into the LSTM network. However, the influence of these factors on PM2.5 concentration is different. To address this issue, a weighted Random Forest (RF)-LSTM model was proposed to predict PM2.5 concentration for the next six hours in this study. This model first uses the RF to select the factors that are more important for predicting PM2.5 concentration and then uses a fully connected neural network to learn the weight value of each factor. Finally, the weighted data is fed into the LSTM network. The model is trained, validated, and tested using hourly air pollutant and meteorological data collected from four monitoring stations in Beijing, China, from November 1, 2019 to February 28, 2022. The prediction performance of the weighted RF-LSTM model was compared to the RF-LSTM and LSTM models. The results show that the RMSE and MAE of the weighted RF-LSTM model are the smallest, and the R2 is the largest for the next six hours' prediction of PM2.5 concentration at four stations. Compared to the LSTM model, the weighted RF-LSTM model decreases RMSE by 2.3%-5.3%, MAE by 5.6%-9.6%, and improves R2 by 2.0%-4.8%, showing that the weighted RF-LSTM model proposed in this study can achieve better prediction performance and has strong generalization ability. [ABSTRACT FROM AUTHOR]

Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote sensing imaging methods to monitor tree species. However, in comparison to the common classification of land cover types, the accuracy of tree species identification is relatively lower. This study focuses on accurately mapping the distribution of planted chestnut forests in China, particularly in the Huairou and Miyun regions, which are the main producing areas for Yanshan chestnuts in northeastern Beijing. We utilized the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery to develop a method based on vegetation phenological features. This method involved identifying three distinct phenological periods of chestnut trees: flowering, fruiting, and dormancy, and extracting relevant spectral, vegetation, and terrain features. With these features, we further established and compared three machine learning algorithms for chestnut species identification: random forest (RF), decision tree (DT), and support vector machine (SVM). Our results indicated that the recognition accuracy of these algorithms ranked in descending order as RF > DT > SVM. We found that combining multiple phenological characteristics significantly improved the accuracy of chestnut forest distribution identification. Using the random forest algorithm and Sentinel-2 phenological features, we achieved an impressive overall accuracy (OA) of 98.78%, a Kappa coefficient of 0.9851, and a user's accuracy (UA) and producer's accuracy (PA) of 97.25% and 98.75%, respectively, for chestnut identification. When compared to field surveys and official area statistics, our method exhibited an accuracy rate of 89.59%. The implementation of this method not only offers crucial data support for soil erosion prevention and control studies in Beijing but also serves as a valuable reference for future research endeavors in this field. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution. [ABSTRACT FROM AUTHOR]

Background: The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. Methods: In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. Results: Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. Conclusions: For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis. [ABSTRACT FROM AUTHOR]

The escalation of anthropogenic heat emissions poses a significant threat to the urban thermal environment as cities continue to develop. However, the impact of urban spatial form on anthropogenic heat flux (AHF) in different urban functional zones (UFZ) has received limited attention. In this study, we employed the energy inventory method and remotely sensed technology to estimate AHF in Beijing's central area and utilized the random forest algorithm for UFZ classification. Subsequently, linear fitting models were developed to analyze the relationship between AHF and urban spatial form indicators across diverse UFZ. The results show that the overall accuracy of the classification was determined to be 87.2%, with a Kappa coefficient of 0.8377, indicating a high level of agreement with the actual situation. The business/commercial zone exhibited the highest average AHF value of 33.13 W m−2 and the maximum AHF value of 338.07 W m−2 among the six land functional zones, indicating that business and commercial areas are the primary sources of anthropogenic heat emissions. The findings reveal substantial variations in the influence of urban spatial form on AHF across different UFZ. Consequently, distinct spatial form control requirements and tailored design strategies are essential for each UFZ. This research highlights the significance of considering urban spatial form in mitigating anthropogenic heat emissions and emphasizes the need for customized planning and renewal approaches in diverse UFZ. [ABSTRACT FROM AUTHOR]

Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performed between the prediction model functioning with a buffer and without one, and the accuracy of the location model was verified by comparing the actual change trend and the predicted trend in two years. The following conclusions were obtained: (1) coffee shops in the main urban area of Beijing are clustered in an area within 12 km of the main urban center, and also around the core commercial agglomeration area; (2) the random forest (RF) model is the best model in this study, and the accuracy values before and after buffer analysis were 0.915 and 0.929, respectively; and (3) after verifying the accuracy of the model through two years of data, we recommend the establishment of a main road buffer zone for site selection, and the success rate of site selection was found to reach 72.97%. This study provides crucial insight for coffee shop prediction model selection and potential store location selection, which is significant to improving the layout of leisure spaces and promoting economic development. [ABSTRACT FROM AUTHOR]

Background: Aberrant N-glycosylation and its involvement in pathogenesis have been reported in endometrial cancer (EC). Nevertheless, the serum N-glycomic signature of EC remains unknown. Here, we investigated serum N-glycome patterns of EC to identify candidate biomarkers. Methods: This study enrolled 34 untreated EC patients and 34 matched healthy controls (HC) from Peking Union Medical College Hospital. State-of-the-art MSbased methods were employed for N-glycans profiling. Multivariate and univariate statistical analyses were used to identify discriminative N-glycans driving classification. Receiver operating characteristic analyses were performed to evaluate classification accuracy. Results: EC patients displayed distinct differences in serum N-glycome and had abnormal high-mannose and hybrid-type N-glycans, fucosylation, galactosylation, and linkage-specific sialylation compared with HC. The glycan panel built with the four most discriminative and biologically important derived N-glycan traits could accurately identify EC (random forest model, the area under the curve [AUC]=0.993 [95%CI 0.955-1]). The performancewas validated by two othermodels. Total hybridtype N-glycans significantly associated with the differentiation types of EC could effectively stratify EC into well- or poorly-differentiated subgroups (AUC>0.8). Conclusion: This study provides the initial evidence supporting the utility of serum N-glycomic signature as potential markers for the diagnosis and phenotyping of EC. [ABSTRACT FROM AUTHOR]

Forest stock volume (FSV) is a key indicator for measuring forest quality, evaluating forest management capabilities, and the main factor for evaluating forest carbon sequestration levels. In this study, to achieve an accurate estimation of FSV, we used Ninth Beijing Forest Inventory data (FID), and Landsat 8 OLI and Sentinel-2 MSI imagery to establish FSV models. The performance of Landsat 8 and Sentinel-2 imagery data in estimating forest volume in Huairou District, Beijing, China was compared. The combination of Landsat 8 and Sentinel-2 satellite data was employed to create a new data source. Two variable selection methods, linear stepwise regression (LSR) and recursive feature elimination (RFE), were used to extract feature variables. The multiple linear regression(MLR) models, Back Propagation (BP) neural network models, and Random Forest (RF) models were employed to estimate forest volume in the study area based on the feature variables obtained from both data sources. The research results indicate (1) the Sentinel-2-based model achieved higher accuracy compared to the same model based on the Landsat 8 factor set. The correlation between the red-edge band of Sentinel-2 imagery and FSV is more significant than that of other characteristic variables used. Variables derived from the red-edge band have the potential to reduce model errors; (2) the estimation accuracy of the model can be significantly improved by using the RFE (Recursive Feature Elimination) method to select remote sensing feature variables. RFE is based on the importance ranking of all feature variables and selects the feature variables that contribute the most to the model. In the variable group selected by RFE, the texture features and the derived features from the red-edge band, such as S e n B 5 , S e n R V I , S e n m N D V I r e , and S e n B 5 M e a n , contribute the most to the improvement of model accuracy. Furthermore, in the optimal Landsat 8–Sentinel-2 RFE-RF model, where texture features are involved, the rRMSE is greatly reduced by 3.7% compared to the joint remote sensing RFE-RF model without texture features; (3) the MLR, BP, and RF models based on the modeling factor set established on Sentinel-2 have accuracy superior to the model accuracy established based on the modeling factor set of Landsat 8. Among them, the Random Forest (RF) method inverted by the recursive feature elimination (RFE) method using Sentinel-2A image has the best inversion accuracy effect ( R 2 = 0.831, R M S E = 12.604 m 3 ha − 1 , r R M S E = 36.411%, M A E = 9.366 m 3 ha − 1 ). Comparing the performance of the models on the test set, the ranking is as follows, Random Forest (RF) model > Back Propagation (BP) neural network model > multiple linear regression (MLR) model. The feature variable screening based on the Random Forest's recursive feature elimination (RFE) method is better than the linear stepwise regression (LSR). Therefore, the RFE-RF method based on the joint variables from Landsat 8 and Sentinel-2 satellite data to establish a new remote sensing data source provides the possibility to improve the estimation accuracy of FSV and provides reference for forest dynamic monitoring. [ABSTRACT FROM AUTHOR]

The damage of expansion joints is a common problem of bridges, which adversely affects not only driving comfort and safety but also the durability of the structure. Jointless bridges can fundamentally solve this fragile problem. However, how to accurately assess its environmental and economic benefits has not been studied, specifically as climate change intensifies. To address these issues, this study first examines the effects of climate change on design expansion allowance of bridge expansion joints based on climate projections. Subsequently, the life-cycle environmental and economic benefits of two jointless bridges located in Beijing–Hong Kong–Macau Expressway are quantified under climate change scenarios. Moreover, uncertainty and random forest algorithm-based importance analyses are carried out to help decision-makers identify major environmental and economic factors and develop eco-friendly plans to facilitate jointless bridge construction. Results indicate the following: (1) the design expansion allowance of bridge expansion joints tends to increase with the time-dependent temperature and humidity; (2) the avoidance of traffic detours caused by the maintenance and replacement of expansion joints is a major source of life-cycle environmental and economic benefits; (3) traffic detour distance is the most important factor influencing the life-cycle environmental and economic benefits. [ABSTRACT FROM AUTHOR]

To explore the genotyping characteristics of human fecal Escherichia coli (E. coli) and the relationships between antibiotic resistance genes (ARGs) and multidrug resistance (MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data. Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs, multilocus sequence typing (MLST), and polymorphism trees were analyzed using whole-genome sequencing data (WGS). This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal (49/70) and healthy groups (15/24). We developed a random forest (RF) prediction model of TEM.1 + baeR + mphA + mphB + QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers. [ABSTRACT FROM AUTHOR]

How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this study to reflect the land use change in urban villages before and after urban renewal by using a hierarchical machine learning recognition method based on scene-based and random forest classification. The overall scale of urban village blocks in Haidian was 10.46 km2, showing the distribution pattern along the traffic arteries in 2013. In 2015, it dropped to 10.11 km2. The scale of urban village blocks in 2020 decreased to 1.02 km2, 9.75% of that in 2013. Three kinds of urban village renewal logic are revealed by further taking Chuanying Village as an example: "urban village–blue–green space", "urban village–real estate", and "urban village–municipal facilities". [ABSTRACT FROM AUTHOR]

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics, which makes them difficult to model complex human mobility patterns. The other is the machine learning models, most of which simply leverage the features of Origin-Destination (OD), ignoring the topological nature of the interaction network and the spatial correlation brought by the nearby areas. In this paper, we propose a 'preprocessing-encoder-decoder' hybrid learning model, which can make full use of geographic semantic information and spatial neighborhood effects, thereby significantly improving the prediction performance. Specifically, in the preprocessing part, we divide the study area into grids, and then incorporates features such as location, population, and land use types. The second step of the encoder designs a convolutional neural network (CNN) to achieve the fusion of neighborhood features, constructs a spatial interaction network with the grids as nodes and the flows as edges, and then uses the graph convolutional network (GCN) to extract the embeddings of the nodes. In the last step of the decoder, a random forest regressor is trained to predict the commuting flow based on the learned embedding vectors. An empirical study on a commuter dataset in Beijing shows that our proposed model is approximately 20% better than XGBoost (state-of-the-art), thus proving its effectiveness. [ABSTRACT FROM AUTHOR]

Air pollution is a problem that directly affects human health, the global environment and the climate. The air quality index (AQI) indicates the degree of air pollution and effect on human health; however, when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored. In the present study, multi-source data were combined to map the distribution of the AQI and population data, and the analyze their pollution population exposure of Beijing in 2018 was analyzed. Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018. Using Luojia-1 nighttime light remote sensing data, population statistics data, the population of Beijing in 2018 and point of interest data, the distribution of the permanent population in Beijing was estimated with a high precision of 200 m × 200 m. Based on the spatialization results of the AQI and population of Beijing, the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level (PWEL) formula. The results show that the southern region of Beijing had a more serious level of air pollution, while the northern region was less polluted. At the same time, the population was found to agglomerate mainly in the central city and the peripheric areas thereof. In the present study, the exposure of different districts and towns in Beijing to pollution was analyzed, based on high resolution population spatialization data, it could take the pollution exposure issue down to each individual town. And we found that towns with higher exposure such as Yongshun Town, Shahe Town and Liyuan Town were all found to have a population of over 200 000 which was much higher than the median population of townships of 51 741 in Beijing. Additionally, the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same, with the peak value being in winter and the lowest value being in summer. The exposure intensity in population clusters was relatively high. To reduce the level and intensity of pollution exposure, relevant departments should strengthen the governance of areas with high AQI, and pay particular attention to population clusters. [ABSTRACT FROM AUTHOR]

Objective: The anti-MDA5 (anti-melanoma differentiation associated gene 5) antibody is often associated with a poor prognosis in juvenile dermatomyositis (JDM) patients. In many developing countries, there is limited ability to access myositis- specific antibodies due to financial and technological issues, especially in remote regions. This study was performed to develop a prediction model for screening anti-MDA5 antibodies in JDM patients with commonly available clinical findings. Methods: A cross-sectional study was undertaken with 152 patients enrolled from the inpatient wards of Beijing Children's Hospital between June 2018 and September 2021. Stepwise logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the random forest (RF) method were used to fit the model. Model discrimination, calibration, and decision curve analysis were performed for validation. Results: The final prediction model included eight clinical variables (gender, fever, alopecia, periungual telangiectasia, digital ulcer, interstitial lung disease, arthritis/arthralgia, and Gottron sign) and four auxiliary results (WBC, CK, CKMB, and ALB). An anti-MDA5 antibody risk probability--predictive nomogram was established with an AUC of 0.975 predicted by the random forest algorithm. The model was internally validated by Harrell's concordance index (0.904), the Brier score (0.052), and a 500 bootstrapped satisfactory calibration curve. According to the net benefit and predicted probability thresholds of decision curve analysis, the established model showed a significantly higher net benefit than the traditional logistic regression model. Conclusion: We developed a prediction model using routine clinical assessments to screen for JDM patients likely to be anti-MDA5 positive. This new tool may effectively predict the detection of anti-MDA5 in these patients using a non-invasive and efficient way. [ABSTRACT FROM AUTHOR]

Battery swapping stations effectively address the challenges of long charging times, lack of charging stations, and safety hazards for electric two-wheelers. With the rapid development of shared electric bicycles and takeaways, the scale of electric two-wheeler users is expanding while generating a huge demand for battery swapping. The research on the planning and operation of battery swapping stations (BSSs) for electric two-wheelers has yet to be widely discussed. This study developed a data-driven optimization model based on machine learning algorithms using Beijing's battery swapping stations and point of interest (POI) dataset. First, through the spatial features of BSS analyzed by ArcGIS, we found that the coverage of BSSs was mainly concentrated within the fifth ring road, and the utilization rate was unbalanced. Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. The final stacking model was constructed by strengthening three single models with an accuracy of 86.21%. Compared with the original BSSs layout, the machine-learning algorithm proposed in this study can cover more factors and avoid the subjectivity of site selection. Finally, the queuing model for BSSs based on the Monte Carlo simulation was proposed. Through two scenarios, we found that the key parameters m (the number of charging slots) and λ (the user arrival rate) were influential to the outputs of service capability. [ABSTRACT FROM AUTHOR]

The timeliness, precision, and low cost of search data have great potential for projecting tourist volume. Obtaining valuable information for decision-making, particularly for predicting, is hampered by the vast amount of search data. A systematic investigation of keyword selection and processing has been conducted. Using Beijing tourist volume as an example, 11 different feature extraction algorithms were selected and combined with long short-term memory (LSTM), random forest (RF) and fuzzy time series (FTS) for forecasting tourist volume. A total of 1612 keywords were retrieved from Baidu Index demand mapping using the direct word extraction method, range word extraction method and empirical selection method. The remaining 813 keywords were subjected to feature extraction. Based on the forecasting results of medium and short-term (1-day, 7-days and 10-days), the forecasting results of Kernel principal component analysis (KPCA) and locally linear embedding (LLE) are relatively stable when the dimensionality is reduced to 5 dimensions. The forecasting results of t-stochastic neighbor embedding (t-SNE), isometric mapping (IsoMap) and locally linear embedding (LLE), locality preserving projections (LPP), independent component correlation (ICA) are relatively stable when the dimensionality is reduced to 10 dimensions. Accurately forecasting many factors (transportation, attraction, food, lodging, travel, tips, tickets, and weather) provides a solid foundation for tourism demand optimization and scientific management and a resource for tourists' holistic vacation planning. [ABSTRACT FROM AUTHOR]

Objective: To explore if random forest (RF) model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression(LR) model. Methods: A total of 254 cases of hospital-acquired Klebsiella pneumoniae infection in a tertiary hospital in Beijing from January 2016 to December 2020 were retrospectively collected. Appropriate influencing factors were selected by referring to relevant articles from the aspects of basic clinical information and contact history before infection, and divided into a training set and a test set. Both the RF and LR models were trained by the training set, and using testing set to compare these two models. Results: The prediction accuracy of the LR model was 87.0%, the true positive rate of the LR model was 94.7%; the false negative rate of the LR model was 5.3%; the false positive rate of the LR model was 35%; the true negative rate of the LR model was 65%; the sensitivity of the LR model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 94.7%; and the specificity was 65%. The prediction accuracy of the RF model was 89.6%; the true positive rate of the RF model was 92.1%; the false negative rate of the RF model was 7.9%; the false positive rate of the RF model was 21.4%; the true negative rate of the RF model was 78.6%; the sensitivity of the RF model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 92.1%; and the specificity was 78.6%. ROC curve shows that the area under curve(AUC) of the LR model was 0.91, and that of the RF model was 0.95. Conclusion: The RF model has higher specificity, sensitivity, and accuracy for the prognostic prediction of hospital-acquired Klebsiella pneumoniae infection than the LR model and has greater clinical application prospects. [ABSTRACT FROM AUTHOR]

Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method. [ABSTRACT FROM AUTHOR]

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s−1, considerably smaller than the result (1.29 m s−1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments. [ABSTRACT FROM AUTHOR]

Random forests are one of the most popular machine learning methods due to their accuracy and variable importance assessment. However, random forests only provide variable importance in a global sense. There is an increasing need for such assessments at a local level, motivated by applications in personalized medicine, policy-making, and bioinformatics. We propose a new nonparametric estimator that pairs the flexible random forest kernel with local sufficient dimension reduction to adapt to a regression function's local structure. This allows us to estimate a meaningful directional local variable importance measure at each prediction point. We develop a computationally efficient fitting procedure and provide sufficient conditions for the recovery of the splitting directions. We demonstrate significant accuracy gains of our proposed estimator over competing methods on simulated and real regression problems. Finally, we apply the proposed method to seasonal particulate matter concentration data collected in Beijing, China, which yields meaningful local importance measures. The methods presented here are available in the drforest Python package. for this article are available online. [ABSTRACT FROM AUTHOR]

Urban scenes consist of visual and semantic features and exhibit spatial relationships among land-use types (e.g. industrial areas are far away from the residential zones). This study applied a graph convolutional network with neighborhood information (henceforth, named the neighbour supporting graph convolutional neural network), to learn spatial relationships for urban scene classification. Furthermore, a co-occurrence analysis with visual and semantic features proceeded to improve the accuracy of urban scene classification. We tested the proposed method with the fifth ring road of Beijing with an overall classification accuracy of 0.827 and a Kappa coefficient of 0.769. In comparison with other methods, such as support vector machine, random forest, and general graph convolutional network, the case study showed that the proposed method improved about 10% in urban scene classification. [ABSTRACT FROM AUTHOR]

Most urban green space (UGS) studies focus on city-level analysis, which is not conducive to urban renewal. We developed a new deep learning approach for characterising the structural evolution of block-scale UGS in urban renewal scenarios. Taking Beijing and Shanghai as models, we first segmented the study areas into block-scale grids and trained a random forest model based on a local climate zone framework to identify the spatial morphological classes of the grids. Second, we established a VGG16-based deep learning model to identify UGS structural types. Finally, we examined the associated patterns of grid spatial morphology and UGS structure under different renewal scenarios. We found that six distinct types of UGS structure are associated with different urban morphological classes. Spatial and temporal variations of UGS structures were observed in terms of direction of conversion, activity level, and extent of improvement to service level. Holistic and micro-renewal approaches drove positive evolution of block-scale UGS structures mainly by reducing building density and via precise optimization, respectively. Our results provide a reference for urban renewal policies and planning guidelines for block-scale UGSs in high-density cities. • DL-based tools identified UGS structure and spatial morphology at the block scale. • Six UGS structural types were identified to be associated with block spatial morphology. • Spatial and temporal differences existed in the structural evolution of block-scale UGSs. • Holistic and micro-urban renewal lead to positive evolution with different drivers. • Negative evolution occurred more frequently in blocks with open morphology. [ABSTRACT FROM AUTHOR]

Objectives：Outdoor advertisement can attract the attention of target user groups and increase brand influence, and site selection is the most important influencing factor for its effectiveness. Reasonable site selection plays a positive role in improving brand awareness and expanding the market. However, due to the difficulty in obtaining commercial data, previous studies on a macro and rough scales failed to conduct detailed analysis on the actual effects of site selection. Methods：We propose a framework to solve the above problems. Firstly, we extract feature sets from geographical attributes and commercial economic attributes and preprocess them using mathematical statistics and the geographical processing basic method. Then, by coupling of the in⁃store conversion rate with road network characteristics, point of interest (POI), and other multi⁃source spatial features, a random forest model is constructed to mine the correlation between them. Finally, the importance of each feature is quantified. Results：We choose Beijing as the study area, and build a prediction model for the in⁃store conversion rate of a home decoration brand based on this framework. The model shows a good performance (Standard R²=0.758). Then we obtain the spatial distribution and factors influencing the suitability of outdoor advertising for this home decoration brand in Beijing. The in⁃store conversion rate of the brand is high in the center and is low in the periphery of Beijing, which features the phenomena of strong spatial autocorrelation and high⁃value aggregation. Meanwhile, the in⁃store conversion rate has a significant correlation with social economy, commercial politics, and crowd activities. The location of continuous advertising exposure to the same group has a great influence on the in⁃store conversion rate. Conclusions：The fine mapping results of the model constructed in this study can quantitatively evaluate the advertising effect of each location and maximize the efficiency, which can provide a reference and theoretical basis for relevant studies on the outdoor advertisement or commercial site selection. [ABSTRACT FROM AUTHOR]

Background: Chronic pruritus is a common problem among older adults, with a significant impact on their quality of life. However, it is under‐studied epidemiologically, especially among Chinese older adults. Objective: The aim of this study was to identify the prevalence and explore the risk factors of chronic pruritus among the middle‐aged and older adults in Beijing, China. Methods: A total of 929 participants aged ≥35 years from six communities in Beijing, China, were interviewed. The survey collected the demographic characteristics, the experience of chronic pruritus (>6 weeks), chronic disease history, the level of physical activities and sleep quality. A population‐based case–control study was conducted, including 178 chronic pruritus cases and 697 controls. A multivariate logistic regression model was performed to explore the risk factors of chronic pruritus. Additionally, a random forest algorithm was used to rank the importance of potential risk factors and analyse the overall interpretation of risk factors. Results: The prevalence of chronic pruritus was 19.48% (181/929) among the Beijing middle‐aged and elderly population. The findings indicated that older adults aged 65 years old or above, male, with college or higher degree, alcohol drinking, hypertension, hyperlipidaemia, chronic lung disease, cardiovascular disease, digestive system disease and osteoarthritis/rheumatism, and middle or low sleep quality were associated with the increased risk of chronic pruritus respectively. Physical activity level (≥3000 Met) was associated with a decreased risk of chronic pruritus. The rank according to the most contribution of chronic pruritus risk was sleep quality, education, physical activity level, osteoarthritis/rheumatism, age and gender. Conclusion: Prevalence of chronic pruritus was high among the Chinese middle‐aged and elderly population. Age, gender, high education, alcohol drinking, hypertension, hyperlipidaemia, chronic lung disease, cardiovascular disease, digestive system disease, osteoarthritis /rheumatism and poor sleep quality may serve as risk factors of chronic pruritus. Moderate and high physical activity levels may serve as protective factors of chronic pruritus risk. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has shown high predictive ability in environmental research. Accurate estimation of daily PM2.5 concentrations is a prerequisite to address environmental public health issues. However, studies on the interpretability of ML algorithms were limited. In this study, we aimed to estimate the daily concentrations of PM2.5 at a seasonal level, and to understand the potential mechanisms of ML algorithms' decisions with SHapley Additive exPlanations (SHAP). Daily ground PM2.5 concentrations and meteorological data were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center, and China Meteorological Data Service Centre between December 2013 and 2019 November. We calculated correlation coefficient and variance inflation factor (VIF) to eliminate the variables with collinearity, and recursive feature elimination (RFE) was further used to selected more important predictors. A series of ML algorithms, including linear regression, the variants of linear regression (Ridge, Lasso, Elasticnet), decision tree (DT), k-nearest neighbor (KNN), support vector regression (SVR), ensemble methods (random forest: RF, eXtreme Gradient Boosting: XGBoost), and deep learning (long short-term memory network: LSTM), were developed to estimate seasonal-level daily PM2.5 concentrations. A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performance to deviation (RPD), and Lin's concordance correlation coefficient (LCCC) were used to evaluate models' performance. SHAP was performed for local and global interpretability analysis. The results showed that the distribution of PM2.5 concentrations in Beijing showed obvious seasonal patterns. A total of five variables (Precipitation, Mean wind speed, Sunshine duration, Mean surface temperature, Mean relative humidity) were selected for final prediction. LSTM showed much higher accuracy than other traditional ML models, achieved the smallest RMSE of 19.58 µg/m3 and MAE of 15.11 µg/m3. In terms of selected data set, there was acceptable (LCCC = 0.41 ~ 0.52) agreement and accuracy (RPD = 0.97 ~ 1.92) for LSTM. The SHAP analyses revealed that the meteorological factors had different influences in specific predictions, and the complex interactions were also illustrated. These results enhance our understanding of meteorological factors-PM2.5 relationships and explain the mechanisms of ML algorithms' decisions. [ABSTRACT FROM AUTHOR]

For afforestation, it is necessary to consider habitat conditions and their impact on specific tree species, in order to enable the selection of appropriate species to improve forest productivity and stand stability. Based on the 2014 Beijing forest management inventory data, we evaluated site quality using theoretical growth equations and quantile regression; we analyzed the effects of climate, topography, and soil variables on the growth of six main tree species using random forest models optimized by a genetic algorithm; and we mapped the potential habitat of six main tree species in Beijing. The results showed that climatic factors were the most important factors affecting tree growth. The prediction models had good accuracy, with an AUC of 0.75–0.85. Among the six main tree species studied, Pinus tabulaeformis Carr. was suitable for all of Beijing's forest land. Platycladus orientalis (Linn.) Franco, Robinia pseudoacacia Linn. and Salix matsudana Koidz. were suitable for the mountainous areas, while Sophora japonica Linn. and Populus tomentosa Carr. were suitable for planting in the plains area of southeast Beijing. The optimized random forest model applied in this study gives insight into the distribution suitability of the main tree species in Beijing, and could serve as a reference for afforestation design. [ABSTRACT FROM AUTHOR]

High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution. [ABSTRACT FROM AUTHOR]

The cutterhead torque and thrust, reflecting the obstruction degree of the geological environment and the behavior of excavation, are the key operating parameters for the tunneling of tunnel boring machines (TBMs). In this paper, a hybrid hidden Markov model (HMM) combined with ensemble learning is proposed to predict the value intervals of the cutterhead torque and thrust based on the historical tunneling data. First, the target variables are encoded into discrete states by means of HMM. Then, ensemble learning models including AdaBoost, random forest (RF), and extreme random tree (ERT) are employed to predict the discrete states. On this basis, the performances of those models are compared under different forms of the same input parameters. Moreover, to further validate the effectiveness and superiority of the proposed method, two excavation datasets including Beijing and Zhengzhou from the actual project under different geological conditions are utilized for comparison. The results show that the ERT outperforms the other models and the corresponding prediction accuracies are up to 0.93 and 0.99 for the cutterhead torque and thrust, respectively. Therefore, the ERT combined with HMM can be used as a valuable prediction tool for predicting the cutterhead torque and thrust, which is of positive significance to alert the operator to judge whether the excavation is normal and assist the intelligent tunneling. [ABSTRACT FROM AUTHOR]

The formation of ground-level ozone (O 3) is dependent on both atmospheric chemical processes and meteorological factors. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O 3 formation sensitivity in Beijing, China, from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O 3 formation. The results showed that the O 3 prediction performance using concentrations of measured/initial VOC species (R2=0.82/0.81) was better than that using total VOC (TVOC) concentrations (R2=0.77). Meanwhile, the RIs of initial VOC species correlated well with their O 3 formation potentials (OFPs), which indicate that the model results can be partially explained by the maximum incremental reactivity (MIR) method. O 3 formation presented a negative response to nitrogen oxides (NOx) and relative humidity (RH), and a positive response to temperature (T), solar radiation (SR), and VOCs. The O 3 isopleth calculated by the RF model was generally comparable with those calculated by the box model. O 3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O 3 sensitivity analysis. [ABSTRACT FROM AUTHOR]

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors. • Assess urban waterlogging susceptibility using three types of data-driven WSAMs. • Evaluate comprehensive performance of WSAMs using a complex evaluation index-DISO. • WELLSVM used unlabeled clusters shows high accuracy with limited flood inventories. • Use LR-based WSAM to reduce the uncertainty of susceptibility assessment. [ABSTRACT FROM AUTHOR]

Understanding the relationships between visual environment (VE) and outdoor jogging is crucial to facilitating effective environmental interventions. However, how the visual environment affects jogging behavior remains unclear due to data and technology limitations. This study aimed to explore the spatially and temporally varying effects of visual environment on jogging behavior. Specifically, street view images and geotagged photos were used to measure the visual environment outside and inside the traffic analysis zone (TAZ), respectively; and massive GPS trajectories collected from a fitness application were utilized to estimate jogging activities. Furthermore, random forest and multiscale geographically weighted regression (MGWR) models were established to examine the impacts of VE on jogging flow in Beijing, China. Results showed that visual greenery, openness, and walkability play an important role in determining jogging behaviors. Effects of some VE variables display distinct scale effects and spatial non-stationarity. Notably, the dual effects or synergistic effects of specific VE factors, such as visual greenery, walkability, between inside and outside the TAZ were identified. Effects of certain VE factors, e.g., visual greenness, openness, on jogging behavior vary across peak-hours, weekdays and weekends, and seasons, respectively. These findings have practical implications in city planning design for building jogging-friendly urban environment. • Relationship between visual environment and outdoor jogging was explored. • Visual greenery, openness and walkability are closer to fitness jogging. • Effects of certain VE variables displays obvious scale effects. • The dual or synergistic effects of specific VE factors were identified. • VE factors affecting jogging presents spatially and temporally variations. [ABSTRACT FROM AUTHOR]

Ecological land is an important guarantee to maintain urban ecological security and sustainable development. Although increasing studies have been brought to ecological land, with few explorations of the relative importance of anthropogenic-natural factors and how they interact to induce the ecological land evolution. This research sought to fill this gap. In this study, 18 factors, including the risk of goaf collapse, fault, prime croplands, were selected from six aspects of topography, geology, climate, accessibility, socio-economic and land control policies. logistic regression (LR) and random forest (RF) models were adopted to identify the anthropogenic and biophysical factors on the dynamic change of ecological land of Mentougou in Beijing from 1990 to 2018. The results show that there was a significant increase in ecological land from 1990 to 2018. The increased area of ecological land reached 102.11 km2 with an increased rate of 0.78, the gravity center of ecological land gradually moved to the northwest. The impact of anthropogenic factors on ecological land was greater than that of natural factors, ecological land was mainly driven by proportion of prime cropland, per capita GDP, land urbanization, temperature, per capita rural income, elevation and aspect factors. Additionally, slope and precipitation were also identified as important predictors for ecological land change. The model comparison suggested that RF can better identify the relationship between ecological land and explanatory variables than LR model. Based on our findings, the implementation of government policies along with anthropogenic factors are the most important variables influencing ecological land change, and the rational planning and allocation of ecological land by Mentougou government are still needed. [ABSTRACT FROM AUTHOR]

Pollution is the most vital and disturbing issues faced in today's world. Above 2000 people die due to diseases whose root cause is pollution. Pollution can be of various forms and each of these types can have different effects on different people. Increased pollution levels are capable of causing mass destruction to the earth as well as to the species residing in it. One of the prevalent environmental challenges right now is air pollution. Air pollution has been noticeable as one of the most important problems of metropolitan regions around the globe, exclusively in Delhi, Beijing and Tehran where its inhabitants and administrators have long been struggling with air pollution impairment such as the health issues of its citizens. With the rapid development in the availability of data and computational technologies, various machine learning techniques have been proposed for predicting air pollution. Air Quality Index can be predicted using both Classification and Regression models. Decision tree, Linear Regression, Support Vector Regression and Random Forest Techniques are implemented in this proposed research work. From the above models, it is evident that, Random Forest Regression based model out performs better in the air quality index prediction with an accuracy of 98%. [ABSTRACT FROM AUTHOR]

The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. Traditional models have difficulty assessing O3 formation sensitivity in a timely manner due to the limitations of flexibility and computational efficiency. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O3 formation sensitivity in Beijing from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O3 formation. The results showed that the O3 prediction performance using initial concentrations of VOC species (R² = 0.87) was better than that using total VOCs (TVOCs) concentrations (R² = 0.77). Meanwhile, the RIs of VOC species correlated well with their O3 formation potentials (OFPs). O3 formation presented a negative response to NOx, PM2.5 and relative humidity, and a positive response to temperature, solar radiation and VOCs. The O3 isopleth curves calculated by the RF model were generally comparable with those calculated by the box model. O3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O3 sensitivity analysis. [ABSTRACT FROM AUTHOR]

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Nitrous acid (HONO) is of great interest due to its contribution to hydroxyl (OH) radicals by self-photolysis. Nowadays, machine learning (ML) algorithms are good at capturing complicated non-linear relationships between predictors and dependent variables. Here, using the whole year of 2018 of observed HONO and related pollutant data at an urban site in Beijing, an ML-RF (random forest) model is carried out to predict HONO concentrations and explore the main factors influencing HONO formation mechanisms. ML-RF models show satisfactory performance during the heating, non-heating and whole year periods with R values of 0.95, 0.96 and 0.95, respectively. Primary emissions and diffusion have an obvious influence on ambient HONO during the heating period, while chemical formation processes such as NO 2 heterogeneous reaction and photolysis of nitrate are important for HONO during the non-heating period with higher RH and stronger solar intensity. O 3 and NH 3 are the most important variables for HONO in both periods, indicating the close relationship of HONO with atmospheric oxidation and the important role of NH 3 in HONO formation processes. Although there are deviations due to some variability in HONO formation mechanisms between years, ML-RF models based on 2018 data are able to roughly predict HONO for three periods in 2017 and 2021. Overall, machine learning with limited meteorological and pollutant parameters offers great advantages in HONO prediction, and it can also provide some clues to improve the chemical mechanisms of HONO by finding related variables of ambient HONO. [Display omitted] • Machine learning predicted HONO shows satisfactory performance in 2018. • O 3 and NH 3 are the most important HONO predictors. • Main HONO sources are different between heating and non-heating periods. • Machine learning model is feasible to predict HONO across years. [ABSTRACT FROM AUTHOR]

• Explainable spatial machining learning framework was established. • Clarified nonlinear relationships between environmental factors and jogging. • Built environment factors play a more important role in affecting jogging behavior. • Nonlinear effects vary across periods, regions, and mobility patterns of jogging. • The findings provide implications for creating a jogging-friendly environment. Outdoor jogging, as a physical exercise beneficial for health, has proliferated worldwide. However, understanding the nonlinear and heterogeneous associations between environmental factors and jogging behavior remains challenging. This study established an explainable spatial machine learning framework combining Geographically Weighted-Random Forest (GW-RF) and SHapley Additive exPlanation (SHAP) model to address the nonlinearity, spatial heterogeneity, and interpretability. Using large-scale GPS trajectories and multi-source big data, this study provides the global and local explanations of nonlinear associations in Beijing, China. Our findings highlight that (1) Built Environment (BE) factors play a more important role than visual landscape factors in determining jogging behavior, with sports facilities such as tracks and parks having higher contributions. (2) All environmental variables, including the BE, visual landscape, and social economics, exhibit nonlinear and threshold effects on jogging behavior. (3) Certain factors such as population density, number of parks, Greening View Index, and Sky View Index exhibit differential effects across different periods, regions, and mobility patterns of jogging. (4) Compared with the Ordinary Least Squares model, Geographically Weighted Regression model, and Random Forest model, GW-RF model demonstrates improved performance in modeling and predicting the jogging flow. The findings have important implications for urban planners seeking to create a supportive environment that promotes outdoor jogging. [ABSTRACT FROM AUTHOR]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]

Building height information is essential for many applications such as urban planning and population density estimation. The building shadow length varies according to seasons, which is shown as different digital number values in multi-temporal images. Thus, the bi-temporal satellite remote sensing images of Sentinel-2 are used to estimate the buildings height in this study. An area of 15 km × 15 km in Beijing, China is taken as the study area. By preprocessing the data, the remaining pixels are split into two parts: 70% as the training data set and the rest as the testing data set. Then, one classification model and three regression models are proposed with using Random Forest (RF) method. Based on the testing data, it shows that the accuracy rate of the classification model has reached 98.4% with the kappa coefficient of 0.93. And the regression models' root-mean-square error (RMSE) is 0.61 floor for 1-6 floors group, 0.41 floor for 7-12 floor group, and 0.98 floor for above 12 floor group. The final RMSE is 1.62 floor with RF models. In general, this study shows the feasibility of using satellite mid-resolution optical image to estimate the building height and provides an important reference for regional building height estimation in the future. [ABSTRACT FROM AUTHOR]

• One-month dockless bikeshare trip data and metro smart card data from the same period in Beijing is used. • The random forest model and accumulated local effects plots were used to examine the nonlinear effects of factors associated with the links between dockless bikeshare and metro trips. • Findings suggest metro ridership and DBS density around service areas are the most crucial factors with certain thresholds and notable joint effects. • The results draw attention to the importance of how bikes are allocated spatially. In this study, we examine the factors associated with the integrated use of dockless bikesharing (DBS) and metros. Special attention is paid to the nonlinear effects of these factors, using machine learning; in this case, a random forest model and accumulated local effects (ALE) plots. We measure the integrated use of DBS and metros based on DBS data and metro smart card data in Beijing. Explanatory variables include metro station characteristics, road infrastructure, public transportation services, land use, and urban density, as well as meteorological variables. We find that metro ridership and DBS density around service areas are the most crucial factors for promoting cycling access/ egress near metro stations. After reaching the thresholds of 6,000 trips for metro ridership and 200 bikes/ per km2 for DBS density, notable joint effects are evident between the two variables, which are negative in egress trips but positive in access trips. Bicycle parking and bus transfer distance and their connectivity to metro stations are important for integrated use. We find that buses will be less competitive when exceeding 25 lines per station. Population density has a nonlinear effect, with a positive association below 9,000 persons/ km2 and a decreasing effect above this level. The land-use mix index was also found to be negatively associated with integrated use, and bicycle lanes are not associated with integrated use. Our results demonstrate that public transit service planning should consider the constraints of resources and public space, and we provide recommendations for governments and stakeholders to reallocate DBS and other public transit services around metro stations. [ABSTRACT FROM AUTHOR]

Combined cooling, heating and power (CCHP) systems have recently received considerable attention, which can meet multiple energy demands of the users. In this study, a novel CCHP system combining the organic flash cycle (OFC) and solar thermal utilization (ST) system is proposed. Two operating strategies based on the following electric load (FEL) are designed for the building complex in Beijing: the FEL strategy with the negative feedback (NFEL) and the FEL strategy considering flexibility (FFEL). The multi-objective optimization of the operating strategy is performed and solved by the genetic algorithm and the random forest algorithm. The results show that the time of the simplified model by the random forest is reduced by 94.5%. The cost of the three strategies decreases and then increases as the power rating of the internal combustion engine (ICE) increases. When the weight factors are inconsistent, the optimal power rating of the ICE increases and the optimal flash temperature decreases for different strategies. The contribution of the heating provision of the NFEL strategy is 8% higher than that of the FEL strategy. The levelized cost of the FFEL strategy is 6.1% and 2.9% lower than the FEL and NFEL strategies. • A novel CCHP system with organic flash cycle and ST system is proposed. • A FEL strategy considering flexibility are designed for the building complex. • A multi-objective optimization is performed using the genetic algorithm. • The random forest algorithm is used to simplify the OFC thermodynamic model. [ABSTRACT FROM AUTHOR]

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km2, and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R2 = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R2 = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps. • A new noise monitoring method, Rotating Mobile Monitoring method, was proposed in this study. • The random forest model performed best (R2 = 0.72, RMSE = 3.28 dB) among the noise prediction models. • A highly dynamic noise map of 1s LAeq was constructed. • Traffic noise can be accurately predicted from photographs of urban roads and knowledge of where the photographs were taken. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols have significant impacts on climate and environment, so the study on aerosol optical properties is of great scientific significance for climate change and atmospheric environment. The variations in column-integrated aerosol optical properties over Beijing were investigated from March 2016 to December 2019 based on the SKYNET data products. Higher values of AOD and AE found in summer indicate that it is important to pay more attention to fine particle pollution in summer. By fitting the AOD data of SKYNET with AERONET at same waveband, we found that monthly variations in aerosol optical depth estimated from SKYNET observation were mostly consistent with the AERONET data. Aerosols were divided into several modes according to optical characteristics, which deepen some insight into aerosol optical and radiative properties and revealed that the weak absorption aerosol dominated and contributed much to the radiation transport in Beijing. Data derived from the polarization lidar CALIPSO were combined with aerosol optical depth (AOD) from SKYNET and these results were consistent, which further identified the scientific of data. The random forest algorithm was used to interpolate the missing AOD 500nm observations. The original results and the interpolation results were fitted with MODIS respectively, which proved that the random forest algorithm can supplement the missing results well. This work will be of great significance in addressing the impacts of aerosols on climate and environment, and will help reduce the uncertainty of the impacts of aerosols on the Earth's radiation budget. All work will provide scientific support for the prevention and control of specific pollution. • Four years aerosol optical characteristics were analyzed based on Beijing SKYNET station. • Lidar and SKYNET data were integrated to distinguish aerosol types. • The random forest algorithm was used to interpolate missing values. [ABSTRACT FROM AUTHOR]