Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

The phenomenon of fly-tipping of bulky waste is becoming increasingly serious with the development of economy and the improvement of living standards. The fly-tipping of bulky waste causes a waste stream with a high proportion of good quality recyclable materials and a wide range of social and environmental problems including damaging the environment and aggravating traffic in a megacity like Beijing. In this study, we analyzed the quantitative spatial distribution of fly-tipping bulky waste in Beijing for the first time and identified its driving forces to explore the fly-tipping rule of bulky waste. We used Anselin's Local Moran I method to reveal the spatial characteristics, and Random Forest machine learning method to examine the driving factors based on multiple data sources such as geographical, population and survey data. The results showed that the spatial agglomeration of fly-tipping presented a typical core edge diffusion spatial distribution pattern. High-High clusters of cases were found in most regions of Dongcheng District and the east part of Xicheng District. In the multifactorial drivers, the three most important driving factors were found to be accommodation, floating population and income level. Surprisingly, any kind of road and educational level had small effect on the fly-tipping case. These results provide a theoretical basis for the government to advance comprehensive prevention and control strategies of bulky waste fly-tipping. And it is helpful to formulate management policies of fly-tipping bulky waste and provide targeted guidance and suggestions for senior decision makers and managers. • Random forest machine learning method was used to analyze bulky waste fly-tipping. • Fly-tipping presented a typical core edge diffusion spatial distribution pattern. • 15 driving factors were extracted from multiple data sources. • Accommodation and floating population had the highest impact on fly-tipping. [ABSTRACT FROM AUTHOR]

The Chinese Spring Festival (CSF) is the most solemn traditional festival in China, and the substantial changes in anthropogenic activities in megacities provide a unique natural experiment to assess the influence of short-term emission changes on air quality. Here we applied a machine learning based random forest algorithm to six-year aerosol composition measurements in urban Beijing during the CSFs of 2012–2020 to quantify the relative contributions of meteorology and emission changes to air quality. Our results demonstrate large variabilities of air pollutants during the CSF due to the meteorological changes and holiday effect. By removing the meteorological effect, we found that the reduced emissions during CSF caused an average decrease of 5.1% for non-refractory PM 2.5 with chloride and primary organic aerosol being the largest (8.8–18.7%) while the changes in secondary species were small. The COVID-19 lockdown during 2020 led to additional reductions of primary species by 16.3–36.8%, yet increases in nitrate and secondary organic aerosol due to enhanced secondary production. Our study has a significant implication that reducing local traffic and cooking emissions is far from enough for mitigating air pollution in winter in megacities due to the nonlinear effect of secondary production and regional transport. A synergetic control of multiple precursors, e.g., NO x and ammonia, is of great importance to reduce secondary aerosol and improve air quality. • Large variabilities of air pollutants during CSF due to the meteorological changes and holiday effect. • The meteorologically normalized changes in secondary species were small. • The COVID-19 lockdown led to additional reductions of primary species by 16.3–36.8%. [ABSTRACT FROM AUTHOR]

China has implemented two national clean air actions in 2013–2017 and 2018–2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013–2020. The observed concentrations of all criteria pollutants except O 3 showed significant declines from 2013 to 2020, with PM 2.5 annual decline rates of 6–9% in most cities. In contrast, O 3 concentrations increased with annual growth rates of 1–9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O 3 to NO 2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O 3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O 3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis. [Display omitted] • We used a random forest method to remove meteorological effect for air quality data. • The deweathered results show smoothed but similar variation in annual trends. • The meteorology can play positive or negative role depending on specific conditions. • Beijing shows positive meteorology in reducing pollutants for two clean air actions. [ABSTRACT FROM AUTHOR]

• Develop an agent-based model of electric vehicle (EV) and vehicle-to-grid (V2G) adoption. • The EV-V2G model can explicitly simulate social influences on the EV and V2G adoption. • Only 3.3% of survey participants were familiar with V2G. • V2G selling price and advertisement were not very influential to the diffusion of V2G. The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent's preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several "what-if" scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment. [ABSTRACT FROM AUTHOR]

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing's metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect. [ABSTRACT FROM AUTHOR]

Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Harmful algal bloom is prevalent in the reclaimed-water-source (RWS) river caused by the excessive nutrient's inputs. Rainfall water may be the sole nutrient-diluted water source for the RWS river. However, the effects of storm events on the algal bloom in the RWS river are poorly understood. This study presents chlorophyll-a (Chl-a) variations before, during, and after the initial storm events (Pre-storm, In-storm, and Post-storm) at four representative sites with distinct hydraulic conditions in a dam-regulated RWS river system, Beijing. The response of Chl-a to the initial storm events mostly depends on the ecosystem status that caused by the river hydraulic properties. The upstream is more river-like and downstream is more lake-like. In the river-like system, elevated water temperature (WT, increased by 2 %) could support the dominating algae (diatom) growth (Chl-a increased by 130 %) from Pre-storm to In-storm period. In the lake-like system, the dominant algae (blue algae) declined (Chl-a sharply decreased by 96%–99 %) due to the lower WT (decreased by 3%–7%) and increased flow velocities from Pre-storm to In-storm period. During the Post-storm period, the dominant algae break out (Chl-a surged by 20%–319 %) in the lake-like system caused by the recovered WT (increased by 3%–6%) and flow velocity. The occurrence of algal bloom can be predicted by the Random Forest (RF) model based on water quality parameters such as total nitrogen (TN). The thresholds of algal bloom for the Pre-storm, In-storm, and Post-storm periods were identified as 30 μg/L, 10 μg/L, and 10 μg/L, respectively. The two driven factors were WT and nitrate (NO 3 -N) for the Pre-storm period and were WT and TN for the In- & Post-storm periods. A higher risk of algal bloom is highlighted during the initial storm events in the RWS river. We propose recommendations for improving water quality in the RWS river systems under the climatic change. [Display omitted] • Reveal algal bloom response to the initial storm event in a reclaimed-water-source river. • The chlorophyll a (Chl-a) shows a big fluctuation during the initial storm events. • Algal bloom caused by the elevated water temperature and sufficient nutrients during post-storm. • The variations of algal bloom can be highly predicated by Random Forest model. [ABSTRACT FROM AUTHOR]

In order to achieve the United Nations 2030 Sustainable Development Goals (SDGs) related to green spaces, monitoring dynamic urban green spaces (UGSs) in cities around the world is crucial. Continuous dynamic UGS mapping is challenged by large computation, time consumption, and energy consumption requirements. Therefore, a fast and automated workflow is needed to produce a high-precision UGS map. In this study, we proposed an automatic workflow to produce up-to-date UGS maps using Otsu's algorithm, a Random Forest (RF) classifier, and the migrating training samples method in the Google Earth Engine (GEE) platform. We took the central urban area of Beijing, China, as the study area to validate this method, and we rapidly obtained an annual UGS map of the central urban area of Beijing from 2016 to 2020. The accuracy assessment results showed that the average overall accuracy (OA) and kappa coefficient (KC) were 96.47% and 94.25%, respectively. Additionally, we used six indicators to measure quality and temporal changes in the UGS spatial distribution between 2016 and 2020. In particular, we evaluated the quality of UGS using the urban greenness index (UGI) and Shannon's diversity index (SHDI) at the pixel level. The experimental results indicate the following: (1) The UGSs in the center of Beijing increased by 48.62 km2 from 2016 to 2020, and the increase was mainly focused in Chaoyang, Fengtai, and Shijingshan Districts. (2) The average proportion of relatively high and above levels (UGI > 0.5) in six districts increased by 2.71% in the study area from 2016 to 2020, and this proportion peaked at 36.04% in 2018. However, our result revealed that the increase was non-linear during this assessment period. (3) Although there was no significant increase or decrease in SHDI values in the study area, the distribution of the SHDI displayed a noticeable fluctuation in the northwest, southwest, and northeast regions of the study area between 2016 and 2020. Furthermore, we discussed and analyzed the influence of population on the spatial distribution of UGSs. We found that three of the five cold spots were located in the east and southeast of Haidian District. Therefore, the proposed workflow could provide rapid mapping and dynamic evaluation of the quality of UGS. [ABSTRACT FROM AUTHOR]