特大自然灾害预测研究的科学方法论   总被引：7，自引：1，他引：6  

任振球 《地球信息科学学报》2000,2(2):1-5

本文讨论了特大自然灾害预测研究中的若干科学方法论问题。指出: 首要关键在于开放系统; 必须开展全方位的整体研究,从中抓住关键因子进行深入研究; 内外因耦合是引起突发性特大自然灾害的主要缘由; 在科学观念上需由过去注重线性、均匀、连续、平均与距平、平滑和数量分析,转变为侧重非线性、非均匀、不连续、奇异、高频脉冲、结构等信息的提取和分析, 以及形与数的统一; 宇宙中还可能存在一种最大最广泛的整体联系。最后,简要讨论了涉及的几个哲学问题。  相似文献   

中国鲤科鱼类游泳能力综合分析和应用     

蔡露  Katopodis Christos  金瑶  黄应平  韩德举  胡望斌  陈小娟  陶江平  侯轶群 《湖泊科学》2022,34(6):1788-1801

该研究利用学术论文数据库筛选出中国鲤科鱼类游泳能力相关论文115篇,并用Origin软件进行了数据统计分析,旨在归纳和分析中国鲤科鱼类游泳能力并建立估算方法,可为正处于规划阶段和可行性研究阶段的过鱼设施流速设计提供依据.结果表明：(1)鱼类游泳速度与鱼体长度具有显著的非线性相关关系,据此建立了一系列幂函数经验公式,可为游泳能力和过鱼设施研究及设计提供依据.(2)依据所得到的经验公式和协方差分析可知,喜流水型鲤科鱼类的游泳能力>广适型>喜静水型.根据该研究得到的经验公式并结合行业规范和文献资料,对正处于规划阶段和可行性研究阶段过鱼设施流速设计的建议如下：西南地区以裂腹鱼成鱼为主要过鱼对象的过鱼设施进鱼口吸引流速范围为0.61～0.76m/s,通道内最高流速阈值为1.28 m/s;长江中下游以四大家鱼成鱼为主要过鱼对象的进鱼口吸引流速范围为0.76～0.93m/s,通道内最高流速阈值为1.49 m/s,以四大家鱼幼鱼为主要过鱼对象的进鱼口吸引流速范围为0.42～0.62 m/s,通道内最高流速阈值可为0.82 m/s.  相似文献   

基于T-S模糊神经网络的信息融合在赤潮预测预警中的应用   总被引：4，自引：0，他引：4  

高强  宋祎  杜忠晓 《海洋技术学报》2006,25(2):103-106

基于T-S模型的模糊神经网络不但具有模糊逻辑和神经网络两者的优点,又具有很好的学习能力。将基于T-S模型的模糊神经网络的信息融合算法应用在赤潮的预测预警中,研究各种理化因子与赤潮藻类浓度间非线性对应规律和有效预测赤潮藻类浓度。仿真实验表明这种方法具有有效的赤潮预测预警功能。  相似文献   

Modelling the spatial distribution of plaice (Pleuronectes platessa), sole (Solea solea) and thornback ray (Raja clavata) in UK waters for marine management and planning     

D.L. Maxwell  V. Stelzenmüller  P.D. Eastwood  S.I. Rogers 《Journal of Sea Research》2009,61(4):258-267

Species distribution maps are needed for ecosystem-based marine management including the development of marine spatial plans. If such maps are based on predictive models then modelling procedures should aim to maximise validation success, and any uncertainty in the predictions needs to be made explicit. We developed a predictive modelling approach to produce robust maps of the distributions of selected marine species at a regional scale. We used 14 years of survey data to map the distributions of plaice, sole and thornback ray in three hydrographic regions comprising parts of the Irish Sea, Celtic Sea and the English Channel with the help of the hybrid technique regression kriging, which combines regression models with geostatistical tools. For each species–region combination we constructed logistic Generalized Linear Models (GLMs) based on presence–absence data using the environmental variables: depth, bottom temperature, bed shear stress and sediment type, as predictors. We selected GLMs using the mean squared error of prediction (MSEP) estimated by cross-validation then conducted a geostatistical analysis of the residuals to incorporate spatial structure in the predictions. In general, we found that species occurrence was positively related to shallow areas, a bed shear stress of between 0 and 1.5 N/m², and the presence of sandy sediment. Predicted species occurrence probabilities were in good agreement with survey observations. This modelling framework selects environmental models based on predictive ability and considers the effect of spatial autocorrelation on predictions, together with the simultaneous presentation of observations, associated uncertainties, and predictions. The potential benefit of these distribution maps to marine management and planning is discussed.  相似文献   

����Kalman�˲���BP�����緽���ڴ�ӱ���Ԥ���е�Ӧ��   总被引：1，自引：0，他引：1  

李捷斌  孔令杰 《大地测量与地球动力学》2009,29(4):124-126

??????????????BP?????緽??????????ι???????????????????????????????BP????????,???????ж?????????С????????????????С????????,???????????????????????????,?÷??????к??????????????????????  相似文献   

����Bayesian����BP�������GPS�߳�ת��   总被引：2，自引：0，他引：2  

张秋昭  张书毕  刘军  王光辉  王波 《大地测量与地球动力学》2009,29(3):84-87

?????BP?????????????????????????????????????Bayesian?????????BP??????????????????????????GPS??????????????????L-M????????????????????????Bayesian??????BP????????????????????????????????????  相似文献   

储层特低渗透成因分析与评价——以安塞油田沿25区块为例   总被引：2，自引：0，他引：2  

高辉  宋广寿  孙卫  任国富  张创  韩宗元 《地球科学进展》2007,22(11):1129-1133

应用测井资料和铸体薄片、扫描电镜、X 衍射、粒度分析、压汞等化验分析资料综合研究了安塞油田沿25区块长6储层的特低渗透成因和影响因素,并评价了储层储集性能。分析表明,该区块储层物性总体较差,发育粒间孔、溶蚀孔,孔隙结构类型属小孔隙、微细喉道型。其特低渗透成因主要受沉积作用和成岩作用的共同影响,具体表现为：不同沉积微相储层的物性差异较大,软组织含量大、粒度细和成分成熟度较低是造成储层特低渗透特征的主要原因。同时成岩压实、胶结作用使得储层的孔隙度、渗透性进一步减小、变差,而溶蚀作用和成岩缝的产生又在很大程度上改善了储层的特低渗透特征。根据物性和孔隙结构参数分析,将研究区储集岩分为3类,水下分流河道与河口坝砂体是主要储层分布相带。  相似文献   

数值模式的预报策略和方法研究进展   总被引：17，自引：1，他引：16  

任宏利  丑纪范 《地球科学进展》2007,22(4):376-385

数值预报经历了半个多世纪的发展,已成为当前主要的客观预报工具。在模式和资料状况给定的情况下,预报效果的改善很大程度上依赖于所采用的预报策略和方法。为此,全面回顾了国内外基于数值模式的预报策略和方法研究进展,认为采取统计—动力相结合、从历史资料中提炼信息的预报策略是提高数值预报水平的可行之路。最后在总结前人工作基础上,着重介绍了动力相似预报策略和方法的相关研究,特别是实际预报中的试验情况。  相似文献   

Prediction models for tidal level including strong meteorologic effects using a neural network     

S.X. Liang  M.C. Li  Z.C. Sun   《Ocean Engineering》2008,35(7):666-675

Accurate prediction of tidal level including strong meteorologic effects is very important for human activities in oceanic and coastal areas. The contribution of non-astronomical components to tidal level may be as significant as that of astronomical components under the weather, such as typhoon and storm surge. The traditional harmonic analysis method and other models based on the analysis of astronomical components do not work well in these situations. This paper describes the Back-Propagation Neural Network (BPNN) approach, and proposes a method of iterative multi-step prediction and the concept of periodical analysis. The prediction among stations shows that the BPNN model can predict the tidal level with great precision regardless of different tide types in different regions. Based on the non-stationary characteristic of hourly tidal record including strong meteorologic effects, three Back-Propagation Neural Network models were developed in order to improve the accuracy of prediction and supplement of tidal records: (1) Difference Neural Network model (DNN) for the supplementing of tidal record; (2) Minus-Mean-Value Neural Network model (MMVNN) for the corresponding prediction between tidal gauge stations; (3) Weather-Data-based Neural Networks model (WDNN) for set up and set down.The results show that the above models perform well in the prediction of tidal level or supplement of tidal record including strong meteorologic effects.  相似文献   

Support vector regression methodology for storm surge predictions   总被引：6，自引：0，他引：6  

S. Rajasekaran  S. Gayathri 《Ocean Engineering》2008,35(16):1578-1587

To avoid property loss and reduce risk caused by typhoon surges, accurate prediction of surge deviation is an important task. Many conventional numerical methods and experimental methods for typhoon surge forecasting have been investigated, but it is still a complex ocean engineering problem. In this paper, support vector regression (SVR), an emerging artificial intelligence tool in forecasting storm surges is applied. The original data of Longdong station at Taiwan ‘invaded directly by the Aere typhoon’ are considered to verify the present model. Comparisons with the numerical methods and neural network indicate that storm surges and surge deviations can be efficiently predicted using SVR.  相似文献