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Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting

This paper describes the identification of effective typhoon characteristics and the development of a new type of hourly reservoir inflow forecasting model with the effective typhoon characteristics. Firstly, a comparison of support vector machines (SVMs), which is a novel kind of neural networks (NNs), and back-propagation networks (BPNs) is made to select an appropriate NN-based model. The results show that SVM-based models are more appropriate than BPN-based models because of their higher accuracy and much higher efficiency. In addition, effective typhoon characteristics for improving forecasting performance are identified from all the collected typhoon information. Then the effective typhoon characteristics (the position of the typhoon and the distance between the typhoon center and the reservoir) are added to the proposed SVM-based models. Next, a performance comparison of models with and without effective typhoon characteristics is conducted to clearly highlight the effects of effective typhoon characteristics on hourly reservoir inflow forecasting. To reach a just conclusion, the performance is evaluated by cross validation, and the improvement in performance due to the addition of effective typhoon characteristics is tested by paired comparison t-tests at the 5% significance level. The results confirm that effective typhoon characteristics do improve the forecasting performance and the improvement increases with increasing lead-time, especially when the rainfall data are not available. For four- to six-hour ahead forecasts, the improvement due to the addition of effective typhoon characteristics increases from 3% to 18% and from 10% to 113% for Categories I (rainfall data are available) and II (rainfall data are not available), respectively. In conclusion, effective typhoon characteristics are recommended as key inputs for reservoir inflow forecasting during typhoons. The proposed SVM-based models with effective typhoon characteristics are expected to provide more accurate forecasts than BPN-based models. The proposed modeling technique is also expected to be useful to support reservoir operation systems and other disaster warning systems.     

地电场布极方式研究

针对南京地震台地电场无斜道测量的布极方式,从地电场的矢量特性着手,经过对观测数据的分析,认为目前普遍采用地电场斜道观测的方式可以取消,增加NS测道和EW测道的测量,加大地震前兆数据的采集,为地震预报提供更多的前兆观测数据。     

支持向量机方法在地震短期预测中的应用

支持向量机方法是基于统计学习理论提出的一种机器学习方法,在处理小样本、非线性问题方面有着很强的优势。而地震的孕育发生过程是一个复杂的非线性动力学系统,地震数据时间序列的变化更呈现非线性、不规则等特征。本文系统地分析了天津及其周边地区多种前兆现象,选取反映2至3个月短期情况的前兆测项,使用支持向量机分类方法建立地震综合预测模型,初步结果表明这种方法在地震短期预测中有一定的应用前景。     

多色彩高空间分辨率遥感影像矢量边缘信息提取算法与应用

相对单波段灰度影像而言,多波段高空间分辨率遥感影像中可用于边缘检测的光谱信息更加丰富。鉴于Canny算子在灰度图像边缘检测中的优越性能,本文利用输出融合策略对其适用于高空间分辨率遥感影像矢量边缘检测作了改进。基于可视化开发平台VC++.NET,编程实现了福州市航拍的高空间分辨率遥感影像红绿蓝三个标准波段在RGB、IHS、Y IQ、YUV、C IELUV色彩空间中对各种地物矢量边缘信息的有效提取。对高空间分辨率遥感影像矢量边缘各分量的分析认为,由于波谱范围差异的影响,在上述色彩空间中不同地物类型边缘检测时响应程度具有显著的不同。本文研究结果表明,该算法参数设置和色彩空间选择对高空间分辨率遥感影像矢量边缘信息提取有较大的影响。     

干湿循环作用下膨胀土裂隙演化规律试验研究

对南阳膨胀土在反复干湿循环作用下的裂隙演化规律进行了室内试验研究。试验中采用烘干法模拟脱湿过程,采用抽气饱和法模拟饱和过程。在脱湿过程中,定时对土样进行称重、定点拍照,以记录裂隙发育状况。利用矢量图技术对裂隙照片进行矢量化处理,以提取裂隙的各种几何要素,继而进行裂隙度计算。裂隙度变化规律分析结果表明,影响裂隙张开程度的关键因素并非土体含水率,而是含水率梯度,而脱湿速率的空间分布、土块渗透特性以及土块尺寸大小则是决定含水率梯度大小的关键因素;膨胀土裂隙在干湿循环的作用下会逐步发育,主要体现在裂隙总面积与总长度的增加,但此作用有限,裂隙的发育达到一定程度以后便会因为土块尺寸过小而停止;反复干湿循环会使土体产生范性变形,该范性变形与完整土块的胀缩特性并无直接联系,其主要成因是裂隙的发育与土体完整性的破坏,裂隙越发育,土体范性变形量越大     

Metal Ion Extraction with Bipyridine Derivatives as Chelating Ligands in Supercritical Carbon Dioxide

Three fluorinated bipyridine ligands have been designed and synthesized as chelating agents for the extraction of metal ions in supercritical CO₂ (sc‐CO₂). The ligand solubilities in sc‐CO₂ were investigated at different temperatures and pressures, and the measured data have been correlated using a semiempirical model. The calculated results showed satisfactory agreement with the experimental data. Based on these data, metal ion extraction with the three compounds as chelating agents in sc‐CO₂ was performed from spiked filter paper, whereby ligand 1 showed the highest extraction efficiency, especially for Ni²⁺ and Cu²⁺. The extraction constants, K_ex, of the three chelating ligands were seen to increase with increasing extraction efficiency for the same metal ion in the same extraction system.     

基于H-P滤波法、ARIMA和VAR模型的库区滑坡位移综合预测

受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型（ARIMA）对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型（VAR）计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。     

非稳定渗流条件下非饱和土质边坡稳定性的矢量和分析法研究

研究可以同时考虑渗流、变形与稳定的非饱和土质边坡稳定性分析方法具有重要的理论意义及工程实用价值。基于Fredlund双应力变量理论进行非饱和非稳定渗流-应力耦合分析,同时与传统非耦合渗流方法确定的孔压场进行对比发现,两种方法确定的浸润线变化过程相近,堤底处孔压的最大相对误差为8.8%,验证了耦合分析结果的可靠性。将基质吸力对强度的贡献纳入黏聚力中得到坡体强度参数的空间分布规律,又基于滑面强度参数时空分布规律借助Matlab平台开发了非稳定渗流条件下非饱和土质边坡稳定性的矢量和法分析程序。以库水位下降条件下堤坡的稳定性分析为例,将不同方法的计算结果进行对比,发现文中方法搜索得到的滑面位置相对于传统方法要深缓,但总体位置相差不大,不同方法对应的安全系数相差不足0.096,验证了所提方法的合理性。     

改进的支持向量机方法在矿山地质环境质量评价中的应用

传统支持向量机（SVM）评价模型中网格搜索法对参数的选择受到主观因素的影响,选用粒子群算法对SVM模型进行优化,并应用改进的SVM模型（PSO-SVM）对长吉图经济区135个矿山进行地质环境质量评价。PSO-SVM模型的评价结果与综合评价结果的相同率（注：该相同率是指两个评价结果相同的个数占所有评价样本数的百分比）达到95.56%,与SVM模型评价结果的相同率达到91.11%。结合研究区实际情况并分析三种评价结果得出,PSO-SVM模型的评价结果更符合实际情况。改进的支持向量机方法能够避免人为因素影响,提高矿山地质环境评价水平,在评价中具有可行性和有效性。基于改进的支持向量机方法评价结果表明,研究区矿山地质环境受矿山开采等人为活动的影响,54.1%的矿山遭受严重破坏（III级）,25.9%为中度破坏（II级）。评价结果可为研究区矿山环境恢复治理提供决策支持。