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学者姓名：肖桂荣
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本文以福州市为研究区,基于四期土地利用数据和生态系统服务价值评估模型,设定耕地保护、城市发展、生态保护和自然发展 4 种情景,运用MCCA模型模拟出 2035 年不同情景下土地利用变化对福州市生态系统服务价值的影响.结果表明,建设用地显著增加会导致耕地、林地和草地总量降低,造成ESV降低.生态保护情景放缓了建设用地扩展速率,林地资源的恢复使其ESV出现了上涨,是最符合福州市未来国土空间规划的土地利用优化最优情景.林地和水域是影响生态质量最显著的两种地类,未来规划过程中应加大对林地和水域的保护与治理.研究结果可为福州市土地利用格局完善和生态环境质量提高提供参考依据.
Keyword ：
MCCA模型 MCCA模型 土地利用模拟 土地利用模拟 多情景 多情景 生态系统服务价值 生态系统服务价值 福州市 福州市
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| GB/T 7714 | 刘凡可 , 肖桂荣 . 多情景模拟下福州市土地利用变化对生态系统服务价值的影响 [J]. | 国土与自然资源研究 , 2025 , (3) : 26-30 . |
| MLA | 刘凡可 等. "多情景模拟下福州市土地利用变化对生态系统服务价值的影响" . | 国土与自然资源研究 3 (2025) : 26-30 . |
| APA | 刘凡可 , 肖桂荣 . 多情景模拟下福州市土地利用变化对生态系统服务价值的影响 . | 国土与自然资源研究 , 2025 , (3) , 26-30 . |
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近岸海域水交换不仅反映区域水体的物理自净能力,还能揭示污染物迁移扩散过程.为探究闽江口水交换特性,基于河口、陆架和海洋沉积物模型(estuarine,coastal and ocean model system with sediments,ECOM-SED)与Lagrange粒子追踪模型,通过实测数据拟合验证,构建了闽江口海域的三维数值模型,在此基础上开展风场、热通量、径流、潮流等外部环境影响因素的模拟实验.结果表明:闽江口水交换具有显著的垂向分层现象,中层水交换能力最强,表层污染物在海表热通量和风场影响下呈北向迁移趋势;川石水道以南水域水体自净能力强于川石水道北部水域;径流对闽江口水交换驱动作用最显著,关闭径流后,各层粒子滞留比例高达 69%～75%.这些发现对于闽江口的污染物治理、水质变化预测等具有指示意义,能够为闽江河口海域环境保护提供科学依据与技术支持.
Keyword ：
ECOMSED ECOMSED 数值模拟 数值模拟 水交换 水交换 粒子追踪 粒子追踪 闽江口 闽江口
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| GB/T 7714 | 胡金红 , 肖桂荣 , 高伟 . 闽江口水交换数值模拟及其动力机制分析 [J]. | 贵州大学学报（自然科学版） , 2025 , 42 (3) : 95-103 . |
| MLA | 胡金红 等. "闽江口水交换数值模拟及其动力机制分析" . | 贵州大学学报（自然科学版） 42 . 3 (2025) : 95-103 . |
| APA | 胡金红 , 肖桂荣 , 高伟 . 闽江口水交换数值模拟及其动力机制分析 . | 贵州大学学报（自然科学版） , 2025 , 42 (3) , 95-103 . |
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[目的]训练样本的质量对模型性能和预测结果有着重要影响,对于小样本数据区域,有限的样本数量及空间分布不均匀可能导致模型无法充分学习致灾要素特征,进一步增加了模型过拟合风险,影响模型预测准确度,因此需要根据区域特点有针对性地采集优化训练样本.[方法]本文提出一种训练样本采样优化方法,将滑坡正样本原型采样(PBS)方法与无监督聚类模型用于训练样本采样,得到筛选扩充的正样本数据集及客观提取的负样本数据集,构建训练样本采集优化的(Sample Optimization,SO)数据集.然后,引入对小样本数据处理效果较好的随机森林(Random Forest,RF)与支持向量机(Support Vector Machine,SVM)构建滑坡易发性评价模型,分别与原始数据(Raw Data,RD)和仅数据扩充(Data Augmentation,DA)的数据集开展对比实验,利用AUC等指标分析模型预测性能,并基于频率比法对滑坡易发性分区结果进行优选.最后,以滑坡灾害样本数据较少的莆田市为例,开展滑坡易发性评价研究,验证本文提出的训练样本采样优化方法的有效性和泛化能力.[结果]采用SO数据集构建的模型相较于基于RD、DA数据集,AUC值分别提升了10.69%与18.23%,说明预测性能皆有明显的提高,意味通过筛选扩充正样本和客观提取负样本数据集可以获得更好的性能,且有效缓解了模型训练过程中的过拟合问题;根据频率比分析结果,SO-RF的极高与高易发区频率比均高于SO-SVM,说明SO-RF比SO-SVM更适合类似莆田市区域的有限小样本滑坡数据的易发性评价.[结论]本文提出的训练样本优化结合机器学习的评价方法具有较高的适用性和准确率,研究成果可为基于机器学习的滑坡易发性评价的训练样本采样方法提供有效思路.
Keyword ：
支持向量机 支持向量机 无监督聚类 无监督聚类 易发性 易发性 正样本扩充 正样本扩充 滑坡 滑坡 莆田市 莆田市 训练样本采样 训练样本采样 随机森林 随机森林
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| GB/T 7714 | 翁铭锴 , 肖桂荣 . 训练样本采样优化与机器学习结合的滑坡易发性评价方法 [J]. | 地球信息科学学报 , 2025 , 27 (5) : 1113-1128 . |
| MLA | 翁铭锴 等. "训练样本采样优化与机器学习结合的滑坡易发性评价方法" . | 地球信息科学学报 27 . 5 (2025) : 1113-1128 . |
| APA | 翁铭锴 , 肖桂荣 . 训练样本采样优化与机器学习结合的滑坡易发性评价方法 . | 地球信息科学学报 , 2025 , 27 (5) , 1113-1128 . |
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[Objectives] The quality of training samples significantly impacts model performance and prediction accuracy. In regions with limited sample data, the small number of samples and their uneven spatial distribution may prevent the model from effectively learning the features of disaster-inducing factors. This increases the risk of overfitting and ultimately affects the accuracy of model predictions. Therefore, it is crucial to collect and optimize training samples based on regional characteristics. [Methods] To address this issue, this study proposes a sampling optimization method for training samples. The method combines the Prototype Sampling (PBS) approach for selecting landslide-positive samples with an unsupervised clustering model for training sample selection. This results in a screened and expanded positive sample dataset and an objectively extracted negative sample dataset, forming an optimized training sample dataset. Subsequently, the Random Forest (RF) and Support Vector Machine (SVM) models, which are well suited for handling small sample data, were employed to construct a landslide susceptibility evaluation model. Comparative experiments were conducted using Raw Data (RD), a dataset with only Data Augmentation (DA), and the optimized dataset. Model prediction performance was assessed using metrics such as the Area Under the Curve (AUC). Additionally, the frequency ratio method was applied to optimize the results of landslide susceptibility zoning. Finally, a case study was conducted in Putian City, where landslide sample data is relatively scarce, to verify the effectiveness and generalization capability of the proposed sampling optimization method. [Results] The results indicate that models trained on the SO dataset achieved AUC improvements of 10.69% and 18.23% compared to those trained on the RD and DA datasets, respectively, demonstrating a significant enhancement in predictive performance. This suggests that selecting and expanding positive samples while objectively extracting negative samples can improve model accuracy and mitigate the overfitting problem during training. Furthermore, the frequency ratio analysis revealed that the SO-RF model achieved higher frequency ratios in regions with extremely high and high susceptibility than the SO-SVM model, indicating that SO-RF is more suitable for evaluating landslide susceptibility in regions with limited landslide sample data, such as Putian City. [Conclusions] The proposed training sample optimization approach, combined with machine learning evaluation methods, demonstrates high applicability and accuracy. Therefore, the findings of this study provide valuable insights into machine learning-based sampling strategies for landslide susceptibility assessment. © 2025 Science Press. All rights reserved.
Keyword ：
Disaster prevention Disaster prevention Support vector machines Support vector machines
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| GB/T 7714 | Weng, Mingkai , Xiao, Guirong . A Landslide Susceptibility Assessment Method Integrating Training Sample Optimization and Machine Learning [J]. | Journal of Geo-Information Science , 2025 , 27 (5) : 1113-1128 . |
| MLA | Weng, Mingkai 等. "A Landslide Susceptibility Assessment Method Integrating Training Sample Optimization and Machine Learning" . | Journal of Geo-Information Science 27 . 5 (2025) : 1113-1128 . |
| APA | Weng, Mingkai , Xiao, Guirong . A Landslide Susceptibility Assessment Method Integrating Training Sample Optimization and Machine Learning . | Journal of Geo-Information Science , 2025 , 27 (5) , 1113-1128 . |
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The construction of China's ecological civilization, known as 'Beautiful China', necessitates implementing precision watershed management through scientifically informed decision-making. This entails optimizing the spatial distribution of watershed best management practices (the so- called BMP scenario) and proposing multistage implementation plans, or roadmaps that align with practical requirements based on the overarching vision of comprehensive water shed management.The'water shed system simulation-scenariooptimization' method frame work (the simulation-and-optimization-based frame work for short) has demonstrated considerable potential in recent years. To address challenges arising from practical applications of this framework, this study systematically conducted the methodological research: (1) proposing a novel watershed process modeling framework that strikes a balance between modeling flexibility and high-performance computing to model and simulate watershed systems efficiently; (2) introducing slope position units as BMP configuration units and enabling dynamic boundary adjustments during scenario optimization, effectively incorporating practical knowledge of watershed management to ensure reasonable outcomes; (3) presenting an optimization method for determining the implementation orders of BMPs that considers stepwise investment constraints, thereby recommending feasible roadmaps that meet practical needs; and (4) designing a user-friendly participatory watershed planning system to facilitate collaborative decision-making among stakeholders. The effectiveness and practical value of these new methods, tools, and prototype systems are validated through application cases in a representative small watershed. This research contributes to advancing precision watershed management and provides valuable insights for sustainable ecological conservation. The methods proposed within the simulation-and-optimization-based framework in this study are universal methods, which means their application does not depend on the specific implementation, such as the watershed process model, the BMP types considered, the designed BMP configuration strategy, and so on. Further studies should be conducted not only to deepen related theory and method research but also to strengthen promotion and application, especially cooperating with local watershed management agents to provide valuable insights for their sustainable ecological conservation. © 2024 Science Press. All rights reserved.
Keyword ：
Computation theory Computation theory Decision making Decision making Decision support systems Decision support systems Ecology Ecology Investments Investments Soil conservation Soil conservation Water conservation Water conservation Water management Water management Watersheds Watersheds
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| GB/T 7714 | Qin, Chengzhi , Zhu, Liangjun , Shen, Shen et al. Methods for supporting decision-making of precision watershed management based on watershed system simulation and scenario optimization [J]. | Acta Geographica Sinica , 2024 , 79 (1) : 58-75 . |
| MLA | Qin, Chengzhi et al. "Methods for supporting decision-making of precision watershed management based on watershed system simulation and scenario optimization" . | Acta Geographica Sinica 79 . 1 (2024) : 58-75 . |
| APA | Qin, Chengzhi , Zhu, Liangjun , Shen, Shen , Wu, Tong , Xiao, Guirong , Wu, Sheng et al. Methods for supporting decision-making of precision watershed management based on watershed system simulation and scenario optimization . | Acta Geographica Sinica , 2024 , 79 (1) , 58-75 . |
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Using location-based services (LBS) data from the third ring area in Fuzhou city, this study analyzes the spatiotemporal characteristics of block vitality by measuring the density of active population gatherings. It explores the spatiotemporal heterogeneity of block vitality based on a geographically and temporally weighted autoregressive model (GTWAR), considering built environment features such as POI density, functional mix, and road network density. Additionally, it investigates their interactions using a geographic detector and compares differences between weekdays and weekends across five daily periods. Key findings include:(1) The overall urban vitality in the region exhibits a spatial pattern of "one core, two centers, multiple sub-centers, and multiple clusters"and undergoes spatiotemporal dynamic changes across five periods, characterized by 'dispersion, agglomeration, deep agglomeration, and dispersion."(2) The marginal effects of POI density, functional mix, and road network density on block vitality exhibit instability in both spatial and temporal dimensions.(3)Interaction effects between two factors exert a more significant impact on block vitality than the effects of individual factors, with interaction types including dual-factor enhancement and nonlinear enhancement.(4)Differences exist in the spatiotemporal characteristics of block vitality, built environment influences, and their interactions between weekdays and weekends. © 2024 The Authors, published by EDP Sciences.
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| GB/T 7714 | You, Y. , Xiao, G. . Research on the Spatiotemporal Characteristics of Block Vitality based on LBS Data and the Impact Mechanisms of the Built Environment -Taking the Third Ring Road in Fuzhou City as an example [未知]. |
| MLA | You, Y. et al. "Research on the Spatiotemporal Characteristics of Block Vitality based on LBS Data and the Impact Mechanisms of the Built Environment -Taking the Third Ring Road in Fuzhou City as an example" [未知]. |
| APA | You, Y. , Xiao, G. . Research on the Spatiotemporal Characteristics of Block Vitality based on LBS Data and the Impact Mechanisms of the Built Environment -Taking the Third Ring Road in Fuzhou City as an example [未知]. |
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The causes of landslide disasters are complex, and landslide susceptibility assessment is of great significance for disaster warning, prevention, and control management. In the previous mapping studies on landslide susceptibility assessment, land use change factor was not considered. This paper proposed a combination of factors for landslide susceptibility assessment by considering land use dynamic change factor. The landslide frequency ratio was used to quantitatively measure the correlation between land use change and landslide development. And Logistic Regression (LR) model was used to compare the prediction ability of the model before and after the introduction of land use change factor. We constructed three machine learning models: Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). We used AUC and other indicators to compare model performance. Finally, we took Sanming City of Fujian Province as the study area and the whole Fujian Province as the verification area to conduct the landslide susceptibility assessment research. The results show that there is a strong correlation between land use change factor and landslide development. The inclusion of land use change factor improves model prediction accuracy, which indicates that it is necessary to introduce dynamic factor in the assessment of landslide susceptibility. The verification results show that RF model has higher prediction accuracy than DT and GBDT. The high landslide prone areas are mainly distributed in the west and central of Sanming City, where the land use change degree is high, and the impact of human activities is great. The low landslide prone areas basically locate in the high-altitude areas with little influence of human activities. This study provides a new research perspective for landslide susceptibility assessment and helps to explore the impact of human activities on disaster formation. © 2023 Journal of Geo-information Science. All rights reserved.
Keyword ：
Adaptive boosting Adaptive boosting Decision trees Decision trees Disaster prevention Disaster prevention Disasters Disasters Forecasting Forecasting Landslides Landslides Land use Land use Logistic regression Logistic regression
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| GB/T 7714 | Lin, Xuanxin , Xiao, Guirong , Zhou, Houbo . Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change [J]. | Journal of Geo-Information Science , 2023 , 25 (5) : 953-966 . |
| MLA | Lin, Xuanxin et al. "Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change" . | Journal of Geo-Information Science 25 . 5 (2023) : 953-966 . |
| APA | Lin, Xuanxin , Xiao, Guirong , Zhou, Houbo . Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change . | Journal of Geo-Information Science , 2023 , 25 (5) , 953-966 . |
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突发公共卫生事件极易引起社会恐慌,新冠肺炎更是全球聚焦的重大热点事件,客观了解疫情期间的公众情绪响应,有利于政府及相关部门合理管控舆情.本研究以疫情流行期间网民微博博文为基础,通过文本挖掘的方式探索疫情期间网民情感倾向,提出一种以卷积神经网络和双向长短期记忆网络为基础,并融合注意力机制的多通道情感极性分析方法.该方法首先对微博文本数据进行分词和停用词的预处理,通过Word2Vec模型获取词向量表达式,使用多通道CNNs-BiLSTM模型抽取多尺度文本特征,融合注意力机制调整特征权重,以语义相关度进行文本情感倾向判断.通过COVID-19微博舆情数据开展实验验证,结果表明,该方法相较于其他基准模型获得了较高的准确率,能够充分利用多维矩阵捕获丰富的文本特征,具有一定的优越性.
Keyword ：
双向长短期记忆网络 双向长短期记忆网络 情感极性分析 情感极性分析 注意力机制 注意力机制 深度学习 深度学习
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| GB/T 7714 | 谢玉惠 , 肖桂荣 . 融合注意力机制的多通道CNNs-BiLSTM情感极性分析方法 [J]. | 小型微型计算机系统 , 2023 , 44 (6) : 1140-1145 . |
| MLA | 谢玉惠 et al. "融合注意力机制的多通道CNNs-BiLSTM情感极性分析方法" . | 小型微型计算机系统 44 . 6 (2023) : 1140-1145 . |
| APA | 谢玉惠 , 肖桂荣 . 融合注意力机制的多通道CNNs-BiLSTM情感极性分析方法 . | 小型微型计算机系统 , 2023 , 44 (6) , 1140-1145 . |
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武夷山国家公园作为全国十个国家公园之一,是全球生物多样性保护的关键地带,它保存了地球同纬度最完整最典型的中亚热带原生性森林生态系统,对其进行生态系统服务价值评估,不仅为国家公园原真性和完整性的保护提供基础,也为国家公园的建设和发展提供数据支撑和决策支持.利用千年生态系统评估方法和改进的当量因子法,将武夷山国家公园服务功能分供给服务、调节服务、支持服务和文化服务四大类及食物生产、原料生产、气体调节等九小类服务功能.再根据研究区的实际情况修订当量因子表,结合计算出的武夷山国家公园标准当量的价值量以及土地利用情况,评估出武夷山国家公园生态系统服务价值.结果表明:武夷山国家公园生态系统服务价值高达67.18亿元;就生态系统而言,所提供的服务价值从高到低依次为:森林(66.58亿元)>水体(0.31亿元)>草地(0.23亿元)>农田(0.034亿元)>湿地(0.026亿元)>裸地(0.001亿元);就生态系统服务类别而言,其服务价值从高到低依次为:调节服务(34.02亿元)>支持服务(20.32亿元)>供给服务(7.86亿元)>文化服务(4.98亿元).经验证,该研究方法及成果合理适用,为我国国家公园体制建立提供了一套生态系统服务价值计算模式.
Keyword ：
土地利用 土地利用 当量因子法 当量因子法 服务价值 服务价值 武夷山国家公园 武夷山国家公园 生态系统 生态系统
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| GB/T 7714 | 沈若兰 , 肖桂荣 . 武夷山国家公园生态系统服务价值评估 [J]. | 生态科学 , 2023 , 42 (2) : 58-65 . |
| MLA | 沈若兰 et al. "武夷山国家公园生态系统服务价值评估" . | 生态科学 42 . 2 (2023) : 58-65 . |
| APA | 沈若兰 , 肖桂荣 . 武夷山国家公园生态系统服务价值评估 . | 生态科学 , 2023 , 42 (2) , 58-65 . |
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突发性地质灾害危险性评估对灾害防治与风险管理具有重要意义.由于不同地区影响灾害发生的因子各不相同,实际评估过程中难以全面客观地选取适宜的评估因子.机器学习对处理灾害系统的高维非线性问题独具优势,但因模型难以调优而评估效果有限.本文尝试提出一种双向优化的滑坡危险性评估方法:在构建因子敏感性指数开展定量敏感性分析的基础上,结合重要性分析、相关性分析、共线性分析构建四维(Four-Dimensional,4D)特征筛选法用于评估因子综合优选;为克服模型难以调优的问题,引入差分进化(Differential Evolution,DE)算法优化支持向量机(Support Vector Machine,SVM)与多层感知机(Multi-Layer Perceptron,MLP)2种推广能力较强的机器学习模型.最后,以福建省滑坡为例,开展评估方法研究.研究表明:4D特征筛选法能更加客观全面地选取适宜性更高的危险性评估因子,从而降低数据维度、减少信息冗余以提升评估模型性能;DE算法对SVM与MLP具有显著的优化效果,有益于增强模型滑坡危险性的评估准确度,DE-SVM、DE-MLP相较于未优化前模型的AUC值分别提升了 4.43％与4.37％;基于双向优化的滑坡危险性评估结果表明,降雨与土地利用类型对福建省滑坡发生具有重要影响作用,福建省滑坡极高危险区普遍年均降雨较高、地形复杂多变,极低危险区主要位于东南沿海一带及闽江流域两侧.本研究为滑坡危险性评估中的影响因子客观选取与机器学习模型调优提供了一定思路.
Keyword ：
危险性 危险性 四维特征筛选法 四维特征筛选法 因子敏感性指数 因子敏感性指数 多层感知机 多层感知机 差分进化算法 差分进化算法 支持向量机 支持向量机 滑坡 滑坡 福建省 福建省
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| GB/T 7714 | 周侯伯 , 肖桂荣 , 林炫歆 et al. 基于特征筛选与差分进化算法优化的滑坡危险性评估方法 [J]. | 地球信息科学学报 , 2022 , 24 (12) : 2373-2388 . |
| MLA | 周侯伯 et al. "基于特征筛选与差分进化算法优化的滑坡危险性评估方法" . | 地球信息科学学报 24 . 12 (2022) : 2373-2388 . |
| APA | 周侯伯 , 肖桂荣 , 林炫歆 , 尹玉环 . 基于特征筛选与差分进化算法优化的滑坡危险性评估方法 . | 地球信息科学学报 , 2022 , 24 (12) , 2373-2388 . |
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