Gas station sites pose potential risks of soil and groundwater contamination, which not only threatens public health and property but may also damage the assets and reputation of businesses and government entities. Given the complex nature of soil and groundwater contamination at gas station sites, this study utilizes field data from basic and environmental information, maintenance information for tank and pipeline monitoring, and environmental monitoring to develop machine learning models for predicting potential contamination risks and evaluating high-impact risk factors. The research employs three machine learning models: XGBoost, LightGBM, and Random Forest (RF). To compare the performance of these models in predicting soil and groundwater contamination, multiple performance metrics were utilized, including Receiver Operating Characteristic (ROC) curves, Precision-Recall graphs, and Confusion Matrix (CM). The Confusion Matrix analysis revealed the following results: accuracy of 85.1-87.4 %, precision of 86.6-88.3 %, recall of 83.0-87.2 %, and F1 score of 84.8-87.8 %. Performance ranking across all metrics consistently showed: XGBoost > LightGBM > RF. The area under the ROC curve and precision-recall curve for the three models were 0.95 (XGBoost), 0.94 (LightGBM), and 0.93 (RF), respectively. While all three machine learning approaches demonstrated satisfactory predictive capabilities, the XGBoost model exhibited optimal performance across all evaluation metrics. This research demonstrates that properly trained machine learning models can serve as effective tools for environmental risk assessment and management. These findings have significant implications for decision-makers in environmental protection, enabling more accurate prediction and control of contamination risks, thereby enhancing the preservation of ecological systems, public health, and property security.

Debris flows are destructive mass movements that pose multifaceted challenges with profound social and environmental implications in the Western Himalayas. For precise modeling and flow behavior prediction, it is essential to understand the rheological characteristics of debris flow material. In the current study, rheological characteristics like yield stress and viscosity were determined by a series of lab tests using a parallel plate setup in a rheometer. An optimized sampling approach created the reconstituted soil samples of finer particles to change the solid volume concentration and volumetric water content (w/c). Later, the feature importance of finer particles in debris flow rheology was determined using a machine learning regressor. Non-Newtonian behavior was shown by each composition and was similar to Herschel-Bulkley's rheological model. The eXtreme Gradient Boosting (XGBoost) regression model was developed for rheological parameters with robust model fitting with R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}{2}$$\end{document} = 0.90 for yield stress and R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}{2}$$\end{document} = 0.94 for viscosity. The model helped in understanding the sensitivity of rheological parameters with solid constitutents of debris flows. The findings showed that water content and silt concentration substantially impacted the debris flow's rheology. The yield stress was more dominated by silt followed by fine sand, whereas water content influenced the viscosity more than any solid concentration. The flow behavior was also affected by the distribution of grain sizes, with finer particles exhibiting higher viscosity and shear stress than coarser particles. These results enhance understanding of debris flow rheology and highlight the complex interplay between geohazards and sustainable development.

Pipes and tunnels are prone to longitudinal deformation under normal faulting. Predicting the active length, defined as the major deformed length, is crucial for the seismic design of pipes and tunnels. However, an accurate prediction method is hard to develop due to a limited number of experimental data. In this study, a large number of numerical simulations are conducted based on a three-dimensional beam-spring model validated by centrifuge tests. The results are fed to XGBoost models to develop a robust prediction method. For ease of application, only the friction angle of soil, burial depth, diameter, and thickness of pipes or tunnels are incorporated as features. A hyperparameter tuning approach integrating grid search and Bayesian optimization was employed in the training process to establish optimal models with comparatively low complexity and high accuracy. A comparison of predictions from the XGBoost models and curves fitted on relative structure-soil stiffness demonstrates that XGBoost models are much superior. The effects of each feature on the predictions were analyzed by employing the SHAP method. The proposed XGBoost models can effectively and efficiently predict the active length of pipes and tunnels with minimal inputs.

针对地形对积雪堆积与合成孔径雷达（SAR）散射能量的影响，该文提出一种通过C波段Sentinel-1 SAR的HH与HV极化后向散射系数和入射角数据以及海拔、坡度和坡向等地形数据反演南北极雪密度的方法。以欧洲中期天气预报中心（ECMWF）提供的南北极雪密度数据作为地面真值，通过XGBoost模型反演雪密度。使用2019—2021年的数据训练反演模型，通过2022年数据验证模型的泛化性能，平均绝对误差（MAE）为25.889 kg/m3,均方根误差（RMSE）为36.497 kg/m3;以东南极洲比利时伊丽莎白公主站、加拿大北极群岛与北冰洋实测数据进一步验证模型性能，MAE为37.514 kg/m3,RMSE为43.287 kg/m3。结合地形信息后南北极雪密度反演精度得到大幅度改善，MAE降低24.219 kg/m3,RMSE降低28.25 kg/m3。所提方法具有大规模雪密度反演的潜力，有利于南北极雪水当量和表面物质平衡的评估。

针对地形对积雪堆积与合成孔径雷达（SAR）散射能量的影响，该文提出一种通过C波段Sentinel-1 SAR的HH与HV极化后向散射系数和入射角数据以及海拔、坡度和坡向等地形数据反演南北极雪密度的方法。以欧洲中期天气预报中心（ECMWF）提供的南北极雪密度数据作为地面真值，通过XGBoost模型反演雪密度。使用2019—2021年的数据训练反演模型，通过2022年数据验证模型的泛化性能，平均绝对误差（MAE）为25.889 kg/m3,均方根误差（RMSE）为36.497 kg/m3;以东南极洲比利时伊丽莎白公主站、加拿大北极群岛与北冰洋实测数据进一步验证模型性能，MAE为37.514 kg/m3,RMSE为43.287 kg/m3。结合地形信息后南北极雪密度反演精度得到大幅度改善，MAE降低24.219 kg/m3,RMSE降低28.25 kg/m3。所提方法具有大规模雪密度反演的潜力，有利于南北极雪水当量和表面物质平衡的评估。

针对地形对积雪堆积与合成孔径雷达（SAR）散射能量的影响，该文提出一种通过C波段Sentinel-1 SAR的HH与HV极化后向散射系数和入射角数据以及海拔、坡度和坡向等地形数据反演南北极雪密度的方法。以欧洲中期天气预报中心（ECMWF）提供的南北极雪密度数据作为地面真值，通过XGBoost模型反演雪密度。使用2019—2021年的数据训练反演模型，通过2022年数据验证模型的泛化性能，平均绝对误差（MAE）为25.889 kg/m3,均方根误差（RMSE）为36.497 kg/m3;以东南极洲比利时伊丽莎白公主站、加拿大北极群岛与北冰洋实测数据进一步验证模型性能，MAE为37.514 kg/m3,RMSE为43.287 kg/m3。结合地形信息后南北极雪密度反演精度得到大幅度改善，MAE降低24.219 kg/m3,RMSE降低28.25 kg/m3。所提方法具有大规模雪密度反演的潜力，有利于南北极雪水当量和表面物质平衡的评估。

针对地形对积雪堆积与合成孔径雷达（SAR）散射能量的影响，该文提出一种通过C波段Sentinel-1 SAR的HH与HV极化后向散射系数和入射角数据以及海拔、坡度和坡向等地形数据反演南北极雪密度的方法。以欧洲中期天气预报中心（ECMWF）提供的南北极雪密度数据作为地面真值，通过XGBoost模型反演雪密度。使用2019—2021年的数据训练反演模型，通过2022年数据验证模型的泛化性能，平均绝对误差（MAE）为25.889 kg/m3,均方根误差（RMSE）为36.497 kg/m3;以东南极洲比利时伊丽莎白公主站、加拿大北极群岛与北冰洋实测数据进一步验证模型性能，MAE为37.514 kg/m3,RMSE为43.287 kg/m3。结合地形信息后南北极雪密度反演精度得到大幅度改善，MAE降低24.219 kg/m3,RMSE降低28.25 kg/m3。所提方法具有大规模雪密度反演的潜力，有利于南北极雪水当量和表面物质平衡的评估。

积雪作为宝贵的淡水资源，其覆盖率的变动对农牧业经济的发展具有深远影响.当前对积雪覆盖率的预测研究较少，为提升积雪覆盖率预测的准确性，基于机器学习算法，构建支持向量回归（SVR）、粒子群（PSO）优化SVR、随机森林（RF）、XGBoost及优化后的XGBoost预测模型对新疆积雪覆盖率进行预测研究，并对模型预测精度进行对比分析.研究结果表明：RF和优化后的XGBoost模型的R2均大于0.9;传统SVR模型的R2均小于0.8,而PSO算法优化后的SVR模型的R2均大于0.8,部分大于0.9;XGBoost模型的R2均低于0.4.说明RF、优化后的XGBoost及PSO-SVR模型在积雪覆盖率预测研究中呈现出较高精度，XGBoost模型的预测结果最差，且利用不同算法对传统模型进行优化在研究中十分必要.

积雪作为宝贵的淡水资源，其覆盖率的变动对农牧业经济的发展具有深远影响.当前对积雪覆盖率的预测研究较少，为提升积雪覆盖率预测的准确性，基于机器学习算法，构建支持向量回归（SVR）、粒子群（PSO）优化SVR、随机森林（RF）、XGBoost及优化后的XGBoost预测模型对新疆积雪覆盖率进行预测研究，并对模型预测精度进行对比分析.研究结果表明：RF和优化后的XGBoost模型的R2均大于0.9;传统SVR模型的R2均小于0.8,而PSO算法优化后的SVR模型的R2均大于0.8,部分大于0.9;XGBoost模型的R2均低于0.4.说明RF、优化后的XGBoost及PSO-SVR模型在积雪覆盖率预测研究中呈现出较高精度，XGBoost模型的预测结果最差，且利用不同算法对传统模型进行优化在研究中十分必要.

积雪作为宝贵的淡水资源，其覆盖率的变动对农牧业经济的发展具有深远影响.当前对积雪覆盖率的预测研究较少，为提升积雪覆盖率预测的准确性，基于机器学习算法，构建支持向量回归（SVR）、粒子群（PSO）优化SVR、随机森林（RF）、XGBoost及优化后的XGBoost预测模型对新疆积雪覆盖率进行预测研究，并对模型预测精度进行对比分析.研究结果表明：RF和优化后的XGBoost模型的R2均大于0.9;传统SVR模型的R2均小于0.8,而PSO算法优化后的SVR模型的R2均大于0.8,部分大于0.9;XGBoost模型的R2均低于0.4.说明RF、优化后的XGBoost及PSO-SVR模型在积雪覆盖率预测研究中呈现出较高精度，XGBoost模型的预测结果最差，且利用不同算法对传统模型进行优化在研究中十分必要.