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961.
962.
963.
Wen-geng Cao Yu Fu Qiu-yao Dong Hai-gang Wang Yu Ren Ze-yan Li Yue-ying Du 《China Geology》2023,81(3):409-419
Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km2 and 3087.45 km2, accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management. 相似文献
964.
965.
针对基于像素的HMRF-FCM算法抗噪性差以及对地物复杂边界分割精度低的问题,提出一种结合形状信息的静态MST区域划分和RHMRF-FCM算法的高分辨率遥感图像分割方法。该方法定义一种静态MST同质区域划分准则,借助MST能较好表达边界和形状信息、能较好抑制几何噪声的特点,解决地物复杂边界的表达和降低分割结果中几何噪声问题。首先,利用MST静态划分将图像域划分成若干个均质区域,假设每个均质区域内光谱测度服从独立同一的多元高斯分布。然后,在此基础上构建了区域隐马尔可夫随机场模型,以及建立基于信息熵和KL信息正则化项的模糊聚类目标函数。最后,采用偏微分方法对分割模型参数进行求解,从而得到全局最优分割结果。为验证本文方法,对WorldView-3高分遥感图像进行分割试验。定性、定量分析了尺度参数、光谱相似性参数和区域紧致度参数对最优分割结果的影响,并对比分析本文算法和eCognition软件中的多分辨率分割算法、分水岭算法。 相似文献
966.
GPS折射率资料在梅雨锋暴雨数值模拟中的应用 总被引:1,自引:0,他引:1
运用WRF三维变分同化系统同化GPS掩星折射率资料,并应用到2007年7月4—6日梅雨锋暴雨个例中进行数值模拟试验,以此考察GPS掩星折射率资料在梅雨锋暴雨数值模拟中的应用。同化结果表明:GPS掩星折射率资料对初始资料的调整主要是中低层湿度场,对于温度场和高度场的调整都是从底层到高层逐渐增大;加入GPS掩星折射率资料对降水预报有所改进,这与中低层湿度场的初始调整有关系;从对模拟结果进行诊断和结构分析中可以发现负散度增量和正涡度增量与降水中心和区域有很好的对应,同化资料之后对于各降水中心地区增强了产生降水的动力条件。 相似文献
967.
萨拉乌苏河流域150 ka BP以来的粒度旋回 总被引:18,自引:1,他引:18
萨拉乌苏河流域米浪沟湾地层剖面150kaBP以来古流动砂丘砂与河湖相和古土壤在粒度上具有多次显著的粗细韵律交替变化,粒度参数-Mz、σ、Sk、Kg和SC/D亦随之响应为多波动峰谷交替的态势,同时,粒度特征值-φ5、φ16φ、φ25、φ50、φ84、φ9相应地表现为非常有节奏地跳动,据此,将米浪沟湾剖面划分为27个粒度粗细沉积旋回,并认为其是150kaBP以来在东亚冬夏古代季风交替影响下沙漠演变的一个颇为完整的气候-地质过程记录。 相似文献
968.
厦门西港引发有害硅藻水华磷的阈值研究 总被引:11,自引:1,他引:11
1995年10月,在厦门市国家海洋局第三海洋研究所的陆基水池中,应用海洋围隔生态系实验技术,研究了在无海流、盐度等因子干扰的情况下,磷的形态、分布及其与浮游植物生物量的相关性。结果表明,海水中的可溶性无机磷(DIP)随时间里指数函数减少。浮游植物生物量主要与可溶性无机磷密切相关:[Chl-a]=A×e-B[PO4],这种相关的浮游植物生物量表现2d左右的滞后现象。还探讨了限制浮游植物增殖的DIP的最低浓度为0.3-04μmol/L,从DIP表现浓度估算浮游植物可能达到的最大生物量为[Chl-a]=10mg/m3,并由此估算出引发中肋骨条藻赤潮的DIP的阈值为1.2μmol/L,作为赤潮预测预报的参考。 相似文献
969.
970.
The debris flow, which was triggered in the Wenjia Gully on August 13, 2010, is an extreme example of mass movement events, which occurred after the Wenchuan earthquake of May 12, 2008. This Earthquake triggered in the Wenjia Gully the second largest co-seismic landslide, which can be classified as a rockslide-debris avalanche. A lot of loose sediments was deposited in the basin. In the main so called Deposition Area II of this landslide, with a volume of 30?×?106?m3, flash floods can easily trigger debris flows because of the steep bottom slope and the relative small grain sizes of the sediments. The largest debris flow of August 13, 2010 destroyed the most downstream dam in the catchment during a heavy rain storm. The debris flow with a peak discharge of 1,530?m3/s and a total volume of 3.1?×?106?m3 caused the death of 7 persons, 5 persons were missing, 39 persons were injured and 479 houses buried. After three rainy seasons, only 16?% of the landslide-debris deposition was taken away by 5 large-scale debris flow events. Since the threshold for rainfall triggered debris flows in the Wenjia Gully and other catchments drastically decreased after the Wenchuan Earthquake, new catastrophic events are expected in the future during the rainy season. 相似文献