基于多元时间序列的Kn近邻预测模型

提出了一种基于多元时间序列的Ｋｎ近邻短期气候预测模型。该模型既保留了Ｋｎ近邻算法的优良特性，同时又考虑了气候系统有关状态量之间的物理关系和动力行为特性。用该模型对云南各区域的平均月雨量距平值进行预测试验，其对云南１９９１年５月～１９９８年６月的月雨量距平值预测检验的距平符号相关准确率为６３．５％，对云南１９９１～１９９８年的５月雨量距平值预测检验的距平符号相关准确率则可达到７０．０％。该模型的建立具有一定的实际业务应用价值。     

青藏高原地面加热场与四川主汛期降水及伏旱关系

分析了高原地面加热场强度距平指数与四川盆地伏旱及主汛期降水的联系。结果表明，高原前期加热强度同四川盆汛期降水和伏旱程度密切相关。把这些关系引入汛期降水预测模型对提高短期气候预测能力有一定积极意义。     

基于神经网络的话务量预测

话务量具有高度的非线性和时变特性,由于神经网络具有较强的非线性映射等特性,将其运用于非线性的话务量短期预测是非常合适的。以青白江2005年10月的话务量作为预测对象,提出基于BP神经网络和基于Elman神经网络的话务量预测模型,仿真实验表明两种模型对于话务量的短期预测均是可行有效的。经过比较,Elman神经网络训练速度比BP神经网络快很多,更适用于实际应用。     

桥梁水平位移混沌特征识别与神经网络预测研究

跨海大桥系统受外界影响扰动,其变形伴有混沌现象发生。对桥梁变形监测数据实现了混沌识别,运用C-C法计算时间序列的延迟时间,用G-P方法求得最佳嵌入维数,通过求取的时间延迟和最佳嵌入维数对桥梁变形监测数据进行相空间重构,为混沌时间序列预测模型的建立奠定基础;基于RBF神经网络建立混沌时间序列预测模型,对实测数据进行桥梁变形水平位移预测,并与基于最大Lyapunov指数混沌时间序列预测结果以及实测数据进行对比分析。结果表明,基于RBF神经网络建立的混沌时间序列预测模型的预测结果比基于最大Lyapunov指数混沌时间序列预测模型的预测结果要好,且短期预测效果好。     

因子集成及综合相似预测模型的研究和应用

针对西南区域（云、贵、川、渝）汛期（６－８月）旱涝预测问题，建立了一种以多种物理因子为基础的西南区域降水量预测模型，即因子集成及综合相似预测模型。该模型既考虑影响西南区域汛期降水的主要物理因素变化，又注重区域要素场自身演变规律。对于影响汛期旱涝的物理因子，采用对多个因素进行综合集成的办法，来体现所有因素对旱涝影响的整体作用。为了强调预测结果的客观性，我们借鉴目前ＮＷＰ集合预报思想，引入综合相似方法，给出西南区域大范围汛期降水距平百分率预测结果。近年来业务试用表明，该方法预测准确率稳定，有明显的预报技巧     

滑坡变形预测灰色神经网络耦合模型的构建及适用性分析

结合灰色模型和神经网络的数据处理特点,提出串联、并联和混联式3种结构的灰色神经网络滑坡变形预测模型。串联式将滑坡变形位移时序分解为趋势项和随机项,采用灰色模型提取滑坡位移时序趋势,利用神经网络逼近随机波动;并联式以灰色模型和神经网络分别对滑坡预测,采用智能非线性组合,按照预测目标精度动态调整权重,从而获取最终组合预测结果;混联式通过增加灰白化层及灰模型群,对神经网络拓扑结构进行优化,达到弱化滑坡原始监测数据随机性、提高预测模型稳健性的目的。将3种模型应用于古树屋滑坡变形预测,并对其适用性进行讨论。结果表明,3种结构的灰色神经网络耦合模型均提高了预测精度,适用于复杂状况下滑坡体的变形预测。     

灰色马尔柯夫预测模型及其在渔获量预测中的应用

为海洋、江河渔获量的短期和长期预测提供一种预测精度较好的预测方法，将灰色ＧＭ（１，１）预测和马尔柯夫概率矩阵预测两者结合起来，通过它们的优点互补，使灰色马尔柯夫预测模型对渔获量的预测结果更科学、更精确。并用灰色马尔柯夫预测模型，预测山东省黄姑鱼的年渔获量。结果是令人满意的。     

青藏高原0．8m地温异常与我国汛期降水的关系

用青藏高原５个站从１９５６～１９８６年共３１年的０．８ｍ地温距平，计算了秋季（９～１１月）的地温异常，同时考虑了我国汛期（４～９月）及夏季（６～８月）的降水相关，发现两者相关很好。不同部位的高原地温异常对我国降水关系的区域是不相同的。本文为中国汛期降水预报提供了依据。     

秦岭北麓现代泥石流灾害活动周期的灰色预测

本文应用灰色系统理论,在对控制泥石流形成的内、外营力要素灰色关联度进行分析的基础上,分别建立了秦岭北麓现代泥石流灾害活动周期的长期、短期及近期灰色动态时间序列预测模型(GM),并进行了灰色灾变预测。结果表明,影响本区泥石流活动的主要因素是雨量强度,其次为地震活动性。并预测出本世纪后半叶泥石流灾害出现的年份为:1962,1966,1982,1988,1994～1995,前四次已与泥石流实际活动年份相符。近期泥石流活动期的预测,对泥石流灾害的预防有实用意义。     

基于深度全连接神经网络的大坝变形预测研究

将深度全连接神经网络引入大坝变形预测领域,结合大坝多源监测数据的训练样本,建立基于深度全连接神经网络的大坝变形预测模型。利用几种常见的深度优化学习算法对模型进行优化训练,通过对比各损失函数的变化曲线选取最优学习算法,进一步构建基于最优学习算法的深度全连接神经网络大坝变形预测模型;最后结合大坝多源监测数据的测试样本对模型进行检验分析,并将预测结果和传统BP神经网络的预测结果进行对比。研究结果表明,本文的深度全连接神经网络模型预测精度高、实用性强,可为大坝安全监控提供参考。     

基于植被状态指数的云南省农业干旱状况时空分析

本文首先计算了云南省2004-2013年农业干旱指数VCI,然后使用Pearson相关系数评价降水与VCI的相关性,基于VCI识别云南省2004-2013年农业干旱事件,最后,与SPEI气象干旱识别结果进行对比分析,在VCI农业干旱识别的基础上,使用干旱频率和干旱面积占比指标分析了云南省2004-2013年农业干旱时空特征。结果表明：降水只是影响VCI指数的关键因素之一;VCI和SPEI指数均能够较好对干旱进行监测并识别典型干旱,但两者的识别结果存在差异;云南省农业干旱频率在春冬两季较高,夏季较低,秋季介于夏季和春冬季之间;春夏冬三季农业干旱频率空间分布较为均匀,秋季农业干旱频率呈南低北高的分布态势,整体上北部干旱频率高于南部;2004-2013年云南省整体干旱面积占比呈现先减小后增加再波动的趋势,春冬两季整体干旱面积占比最高,分别为46.63%和47.18%,呈现下降趋势,夏季整体干旱面积占比最低,为43.81%,呈现上升趋势,秋季整体干旱面积占比介于冬春季和夏季之间,为45.74%,呈现下降趋势。总之,云南省农业干旱春冬易发性最高,影响范围最大,夏季易发性最低,影响范围最小。     

Comprehensive drought monitoring in Yunnan Province,China using multisource remote sensing data

Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.     

基于WebGIS云南滑坡灾害气象预警分析模型

针对云南主要由降雨引起的滑坡地质灾害现象,对云南近8年来的滑坡灾害数据和降雨量数据进行了分析,研究并设计了一种针对滑坡地质灾害发生的时空耦合预警分析模型。并在此基础上,通过与WebGIS技术的耦合,运用新一代的RIA技术Silverlight,基于MapGIS IGServer平台,在异步响应的网络中使用流程化的操作方式构建出云南省地质灾害气象预警系统。通过实例检验表明该滑坡地质灾害气象预警分析模型的应用,能够为相关部门提供更精确、及时的减灾防灾对策。     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

基于湖南CORS网的PWV时空变化分析及其在暴雨落区预报中的应用

针对2017-05-22~05-23湖南地区的一次暴雨过程，结合气温、气压等大气热动力条件，分析此次降雨过程的大气可降水量(precipitable water vapor，PWV)时空分布及其动态变化特征。研究表明，在水汽条件充足的情况下，降雨的形成需要强烈的水汽辐合上升作用，可以结合热动力条件来预判降水时间和强度；PWV与实际降雨量平面分布图的对比分析表明，PWV高值区和实际降雨落区基本重合，PWV时空变化分析对预测暴雨落区有一定的指示意义。     

Annual precipitation gray forecast in disaster year of Chedaren debris flow

The Chedaren ravine belongs to high-prone areas of debris flow in Jilin Province, which threaten the local people＇ s life and security seriously. The authors used the residual correction theory to amend the GM （ 1, 1 ） model and forecast annual precipitation in disaster year of the Chedaren ravine ; it provides scientific foundation for early warning of debris flow disaster in the rainy season based on weather forecast. The prediction resuits show that annual precipitation is 724.7 mm in 2009 ; the region will probably occur large-scale debris flow during the rainy season.     

Annual precipitation gray forecast in disaster year of Chedaren debris flow

The Chedaren ravine belongs to high-prone areas of debris flow in Jilin Province,which threaten the local people's life and security seriously. The authors used the residual correction theory to amend the GM (1, 1) model and forecast annual precipitation in disaster year of the Chedaren ravine; it provides scientific foundation for early warning of debris flow disaster in the rainy season based on weather forecast. The prediction results show that annual precipitation is 724.7 mm in 2009; the region will probably occur large-scale debris flow during the rainy season.     

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湘中一次大暴雨天气的综合诊断分析

利用NCEP的1°×1°再分析资料、FY-2E逐小时TBB资料以及常规气象观测资料,对湘中地区2010年5月12—13日大暴雨过程的环流形势、物理量场以及中尺度系统进行综合分析,试图从多角度揭示这次大暴雨天气的成因。结果表明：高空南支槽,中低层切变以及地面冷空气是这次大暴雨天气过程的主要影响系统;低空急流作为暖湿气流的载体,为暴雨的产生提供了充足的能量条件和水汽条件;冷空气侵入高能不稳定的暖湿气团,是触发暴雨不稳定能量释放的热力机制;z-螺旋度的空间分布能反映暴雨发生时大气的动力特征,中低层强辐合,高层强辐散与低层正涡度的空间结构,有利于高低层形成“抽吸作用”,从而使上升运动得到发展和加强,为暴雨的产生提供有利的动力条件;高分辨率的云顶亮温TBB资料对降水的强度及落区有很好的指示意义。     

Magnitude-frequency relationship of debris flows in the Jiangjia Gully,China

The magnitude-frequency(MF) relationship of debris flows is the basis for engineering designs and risk quantification. However, because of the lack of debris flow monitoring data, research progress in this area has been relatively slow. The MF relationship of debris flows in Jiangjia Gully, Yunnan Province was evaluated based on a regression analysis of 178 debris flow events that occurred from 1987-2004. The magnitude-cumulative frequency(MCF) relationship of the debris flows in the Jiangjia Gully is consistent with the linear logarithmic transformation function. Moreover, observed data for debris flows in Hunshui Gully of Yunnan Province and Huoshao Gully, Liuwan Gully, and Niwan Gully of Gansu Province were used to verify the function. The results showed that the MCF relationship of highfrequency debris flows is consistent with the power law equation, although the regression coefficients in the equation are considerably different. Further analysis showed a strong correlation between the differences in the constants and the drainage area and daily maximum precipitation.