湖泊富营养化是目前公众和政府关注的热点问题之一。热红外遥感技术被广泛应用于识别入湖地下水排泄区，但传统热红外遥感方法并未考虑冻结湖泊表面覆盖的积雪和冰层对反演湖水表面温度的影响，限制了其识别入湖地下水排泄区的精度和适用性。基于遗传算法-支持向量回归（geneticalgorithm-supportvector regression,GA-SVR）模型和中分辨率成像光谱仪（moderate resolution imaging spectoradiometer,MODIS）遥感数据开展了对东北季节性冻土平原区典型湖泊查干湖湖水表面温度的反演与预测研究，识别了不同时期入湖地下水的排泄区。结果表明：GA-SVR模型可将冰封期热红外遥感法反演湖水表面温度的R2由0.69提高到0.95，其识别的入湖地下水排泄区与湖泊中高222Rn浓度的分布区域一致。研究结果可为有效识别查干湖营养物质主要来源和查干湖水环境安全管控提供科技支撑。
湖泊富营养化是目前公众和政府关注的热点问题之一。热红外遥感技术被广泛应用于识别入湖地下水排泄区，但传统热红外遥感方法并未考虑冻结湖泊表面覆盖的积雪和冰层对反演湖水表面温度的影响，限制了其识别入湖地下水排泄区的精度和适用性。基于遗传算法-支持向量回归（geneticalgorithm-supportvector regression,GA-SVR）模型和中分辨率成像光谱仪（moderate resolution imaging spectoradiometer,MODIS）遥感数据开展了对东北季节性冻土平原区典型湖泊查干湖湖水表面温度的反演与预测研究，识别了不同时期入湖地下水的排泄区。结果表明：GA-SVR模型可将冰封期热红外遥感法反演湖水表面温度的R2由0.69提高到0.95，其识别的入湖地下水排泄区与湖泊中高222Rn浓度的分布区域一致。研究结果可为有效识别查干湖营养物质主要来源和查干湖水环境安全管控提供科技支撑。
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.
Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length -slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.