High ground - level ozone (O₃) concentrations severely undermine urban air quality and threaten human health, creating an urgent need for precise and effective ozone - level predictions to aid environmental monitoring and policy - making.This study incorporated the historical concentrations of ozone and nitrogen dioxide (NO₂) from the past 3 hours as lagged features into a Lagged Feature Prediction Model (LFPM), evaluated using nine machine - learning algorithms (including XGBoost). Initially, XGBoost combined with SHAP identified 11 key features, boosting computational efficiency by 30% without sacrificing prediction accuracy. Then, ozone concentrations were predicted using six meteorological variables.Results showed that LSTM - based methods, especially ED - LSTM, performed best among meteorological - only models (R² = 0.479). Yet, predictions based solely on meteorological variables had limited accuracy. Adding five pollutant variables markedly improved the predictive performance across all machine - learning methods. XGBoost achieved the highest accuracy (R² = 0.767, RMSE = 11.35 μg/m³), a 125% relative improvement in R² compared to meteorological - variable - only predictions. Further application of the LFPM model enhanced prediction accuracy for all nine machine - learning methods, with XGBoost still leading (R² = 0.873, RMSE = 8.17 μg/m³).These findings conclusively demonstrate that integrating lagged feature variables significantly enhances ozone prediction accuracy, offering stronger support for environmental monitoring and policy - formulation.

Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the ANN, the particle swarm optimization (PSO) algorithm, and the principal component analysis (PCA) method, a novel PCA-PANN model is proposed. Then, a dataset of 307 slope cases covering a wide range of slope geometries and mechanical properties of geomaterial is developed. The hybrid machine learning model trained with the dataset is applied to the factor of safety (FoS) prediction of the actual slope, and three evaluation indicators are introduced to measure the prediction performance of the model. Finally, the sensitivity analysis of input parameters is carried out, and the slope protection strategy for different sensitive factors is proposed. The results show that this new model can quickly obtain the FoS and stable state of the slope without complex calculation, only by providing the relevant characteristic parameters. The correlation coefficient of the PCA-PANN model for slope stability analysis reaches more than 0.97. The sensitivity degree of influencing factors from large to small is slope angle, cohesion, pore pressure ratio, slope height, unit weight, and friction angle.

Considering that a jointed rock mass in a cold area is often affected by periodic freeze–thaw cycles and shear failure, definitions for the mesoscopic and macroscopic damage to a jointed rock mass under the coupling of freeze–thaw and shear are proposed, and the damage mechanism is verified according to experimental results. The results show that: (1) the jointed rock specimens increase macro-joints and meso-defects, the mechanical properties deteriorate significantly under freeze–thaw cycles, and the damage degree becomes more and more significant with the increases in freeze–thaw cycles and joint persistency. (2) When the number of freeze–thaw cycles is constant, the total damage variable value gradually increases with the increase in joint persistency. The damage variable difference in specimens with different persistency is distinct, which is gradually reduced in the later cycles, indicating a weakening influence of persistency on the total damage variable. (3) The shear resistance of non-persistent jointed rock mass in a cold area is determined by the coupling effect of meso-damage and frost heaving macro-damage. The coupling damage variable can accurately describe the damage variation law of jointed rock mass under freeze–thaw cycles and shear load.

Remote sensing monitoring and geomorphologic change analysis of coral reefs are of great practical significance for the ecological protection and sustainable development of coral reef area resources. A WBMD scheme is proposed to better extract coral reef information from island environments of Xisha, China. Satellite imagery, after preprocessing, is subjected to water depth correction to mitigate the influence of water depth on reflectance. A maximum likelihood classification model is then used for geomorphological classification, followed by refinement through a decision tree classification model, forming the WBMD scheme. The overall accuracy of geomorphological classification for Xisha Chau and Zhaoshu Island based on the WBMD scheme was 97.07% and 95.07%. Using this scheme to analyze the geomorphology of Xisha Chau and Zhaoshu Island from 2014 to 2018 reveals that the degradation of coral reef on Xisha Chau is mainly distributed in the lagoon slope and around the lagoon, and the lagoon area on Zhaoshu Island has been shrinking year by year, while the area of the gray sand island has increased, and the areas of other geomorphological types exhibit fluctuating changes. Previous studies on coral reefs have indicated that coral reefs are mainly distributed in the lagoon slope area, where significant coral reef degradation has also been observed, raising an alarm for coral reef conservation efforts in China.