沉积物粒度组分空间预测的神经网络残余kriging方法

针对近海表层沉积物粒度组分空间变异的尺度差异性,提出了基于广义回归神经网络残余kriging的沉积物粒度组分空间预测方法,并以海州湾沉积物粒度数据为基础,分析了其在沉积物粒度组分空间预测和底质类型制图中的应用效果。结果表明,广义回归神经网络残余kriging方法能够获得比普通kriging方法更高的沉积物粒度组分空间预测精度,并且其底质类型的总体空间预测精度达到85%以上,相应的Kappa系数也在0.8以上,显示底质制图的预测类型与样本的实测类型具有较高的一致性。新方法对于开展定量化的沉积物粒度组分空间预测和底质类型制图具有参考价值。     

基于多元线性回归方法的砌体结构加固费用估算模型研究

为建立适用于砌体结构的加固费用快速估算模型,对中国陕西、山西、四川等地砌体结构建筑物的历史加固费用及相关建筑参数进行统计,建立涵盖35栋砌体结构建筑物加固费用估算模型的回归与验证数据库,同时对影响加固费用的各建筑参数作显著性分析,并基于后向消去的多元线性回归方法,利用SPSS统计分析软件对已采集的加固数据进行回归,得到4个费用估算模型;按照相应评价准则分别对各模型进行评价,提出一套最优的砌体结构加固费用快速估算模型,并对该回归模型进行验证分析。回归得到的费用估算模型满足精度要求,具有一定的工程应用价值。     

Establishing absorbed energy spectra—an attenuation approach

For energy‐based seismic design, energy demand in the form of absorbed energy spectra was established by an attenuation relationship. The absorbed energy is proposed for evaluating the energy demand in an inelastic system because the absorbed energy is directly related to the pseudo‐velocity in the elastic case. Based on a total of 273 ground motion records from 15 significant earthquakes in California, an attenuation relationship of the absorbed energy was established from a two‐stage non‐linear regression analysis. This relationship was established for a given earthquake magnitude, source‐to‐site distance, site class, and ductility factor. A similar expression for the normalized absorbed energy was also developed. This study showed that the absorbed energy for near‐field ground motions can be significantly larger than that predicted by the attenuation relationship for normal ground motions. Copyright © 2000 John Wiley & Sons, Ltd.     

半参数模型稳健估计及其应用

在半参数模型估计中,均假设观测误差服从正态分布。当观测量含有粗差时,粗差对参数和非参数估计的影响是不可忽略。基于此,首先在总结线性参数模型稳健估计基本理论的基础上,论述了M估计权因子的确定方法。然后提出了半参数模型稳健估计方法,并导出半参数模型(广义)补偿最小二乘稳健估计的基本公式。最后通过两个模拟算例验证了其估计方法的有效性。     

中国太阳总辐射的多元逐步回归模拟

研究采用年日照时数、年均总云量、年均水汽压、维度等常规气象要素及地理要素为解析变量,建立起估算全国范围内太阳年辐射总量的多元逐步回归模拟模型;利用全国730多个地面站1951—2002年间的常规气象观测资料,通过所构建的模型对中国太阳年辐射总量进行模拟分析,模型模拟分辨率为1km×1km,规模为4173×4847格网。模型数值计算结果显示:中国太阳辐射资源最丰富的地区为西藏自治区、青海东北部及甘肃西部边境等,高达6700MJ·m-2·a-1以上(一类地区),总面积约130万km2;最低的为四川盆地、青藏高原南部的雅鲁藏布峡谷地区,低于4200MJ·m-2·a-1(五类地区),总面积约75万km2;中国陆地表面每年接受的总太阳辐射能约为52.4×1018kJ;单位面积上太阳年辐射总量约介于2780—7560MJ·m-2·a-1;分布具有明显的地域性,呈现西北、北部高,东南、东部及东北的部分地区低的特点,模拟值较好拟合实际的太阳年辐射总量。研究所构建模型实现了太阳年辐射总量空间分布的连续模拟,模型在保证足够的模拟精度条件下,提高了运算效率,简化了模拟过程,适于全国尺度下太阳年辐射总量的数值模拟计算。     

线形工程中GPS高程拟合方法的探讨

介绍了多项式曲线拟合法和三次样条拟合法用于GPS水准联测点按线状或带状布设的情况,并通过实例简要分析了其优缺点。     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

基于GIS的白龙江流域舟曲—武都段的滑坡危险性评价

本研究以围绕着白龙江流域的甘肃省南部的宕昌县、舟曲县和武都区部分地区为研究区,根据全国滑坡编目中得到的272个历史滑坡数据以及选取的高程、坡度、坡向、平面曲率、剖面曲率、归一化植被指数（NDVI）、降雨、岩性、距道路距离和距河流距离10种影响因子,利用三种具有代表性的定量方法：信息量模型、以及基于频率比模型的逻辑回归模型和人工神经网络模型对研究区内滑坡灾害危险性进行评价。三种评价结果均显示研究区内滑坡灾害的极高和高危险区主要沿白龙江河谷地区呈带状分布。从危险性分区图可看出,人工神经网络模型得到的分区图较为合理,既表现出沿河谷地区集中分布的趋势,也呈现出对滑坡历史数据较为独立的特征,这一研究结果与前人研究结果一致。根据受试者工作特征曲线（ROC曲线）对三种模型的精度进行检验,检验得到的AUC值分别为0.818、0.829和0.837,说明三种评价结果均具有较高的可靠性,基于频率比模型的人工神经网络模型相比其他两个模型具有更好的评价精度,能更好地进行滑坡危险性的预测和评价,其中高程、降雨、岩性以及距道路距离对评价结果影响更大,这四种影响因子重要性值占比为52.1%。为该地区的城市扩建与灾害预防预测提供了参考。     

金沙江下游支流黑水河松新电站幼鱼下行过坝研究

梯级小水电的开发一定程度上破坏了河流连通性并阻碍了鱼类洄游通道。黑水河流域松新坝址为了满足鱼类上行产卵修建了松新鱼道,然而作为洄游物种生命周期中的关键行为,鱼类下行洄游的需求也同等重要。为了寻求一种可行、高效、低损伤的下行过鱼方式,了解河流中不同环境因素对鱼类下行效率的影响,本研究在金沙江下游支流黑水河松新水电站补水通道开展鱼类下行过坝效果评估研究。针对两种鲤科鱼类(棒花鱼Abbottina rivularis和短须裂腹鱼Schizothorax wangchiachii)幼鱼和两种鳅科鱼类(短体副鳅Paracobitis potanini和红尾副鳅Paracobitis variegatus)幼鱼各放鱼90尾,定量分析鱼种、水体透明度、放鱼位置、流量、水温、水位等因素对鱼类下行效率的影响。结果表明:棒花鱼、短须裂腹鱼、短体副鳅、红尾副鳅的下行率依次为22.22%(20尾)、28.89%(26尾)、8.89%(8尾)、15.56%(14尾);幼鱼下行成功率与上游河道水温呈正相关趋势,且放鱼位置、鱼种、上游河道水温和流量均对鱼类下行成功率有显著影响。通过构建二元Logistic回归模型识...     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.