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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]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• 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]

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]

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]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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 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]