软件模块故障倾向预测方法研究   总被引：2，自引：0，他引：2  

罗云锋  普杰  贲可荣 《武汉大学学报(信息科学版)》2010,(5)

研究了在区分故障严重程度下的软件模块故障倾向预测方法,将故障分为高严重程度和低严重程度两种类型,用统计分析和机器学习方法分析静态代码度量与故障倾向之间的关系。以公开和私有两种类型的失效数据集作为实验数据,分析发现,故障的严重程度影响预测性能,预测不同严重程度的故障需要选择不同的度量和分类模型,预测低严重程度故障的性能好于预测高严重程度故障的性能。  相似文献   

基于年际增量法的卷积神经网络模型对中国夏季降水的预测研究          下载免费PDF全文

蒋鹏  胡轶佳  钟中  孙源  吕硕 《气象科学》2023,43(5):569-577

将前冬的500 hPa位势高度、向外长波辐射和海表温度的年际增量作为预测因子，建立基于卷积神经网络（Convolutional Neural Network，CNN）的非线性预测模型，对中国160个测站夏季降水展开预测研究，并与基于线性奇异值分解（Singular Value Decomposition，SVD）的预测模型进行效果对比。结果表明：CNN在1981—2020年的交叉检验中所回报的降水平均PS评分和距平相关系数（ACC）分别为74.33和0.12，比SVD高2.15和0.06，说明CNN比SVD在整体上对夏季降水具有更好的预测能力。其中，CNN对SVD预测较好年份的预测效果提升较为明显，对SVD预测较差的年份则改进不大。CNN对中国降水预测存在一定的系统性偏差，订正后CNN对拉尼娜年的降水预测改进较大。结果表明，基于年际增量法的CNN预测模型展示出较好的潜在应用价值。  相似文献   

Analysis of flood propagation changes in the Kienstock—Bratislava reach of the Danube River/Analyse des changements de propagation des crues dans le tronçon Kienstock—Bratislava du Fleuve Danube     

《水文科学杂志》2013,58(4)

Abstract

In the first part of this study, a flood wave transformation analysis for the largest historical floods in the Danube River reach Kienstock–Bratislava was carried out. For the simulation of the historical (1899 and 1954) flood propagation, the nonlinear river model NLN-Danube (calibrated on the recent river reach conditions) was used. It was shown that the simulated peak discharges were not changed significantly when compared to their historical counterparts. However, the simulated hydrographs exhibit a significant acceleration of the flood wave movement at discharges of between 5000 and 9000 m³ s^-1. In the second part, the travel time-water level relationships between Kienstock and Bratislava were analysed on a dataset of the flood peak water levels for the period 1991–2002. An empirical regression routing scheme for the Danube short-term water level forecast at Bratislava station was derived. This is based on the measured water level at Kienstock gauging station.  相似文献   

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors     

Zhengzheng Zhou  Jie Ren  Xiaogang He  Shuguang Liu 《水文研究》2021,35(11):e14424

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.  相似文献   

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin,Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero,en Tanzanie     

《水文科学杂志》2013,58(5)

Abstract

Modelling of the rainfall–runoff transformation process and routing of river flows in the Kilombero River basin and its five sub-catchments within the Rufiji River basin in Tanzania was undertaken using three system (black-box) models—a simple linear model, a linear perturbation model and a linear varying gain factor model—in their linear transfer function forms. A lumped conceptual model—the soil moisture accounting and routing model—was also applied to the sub-catchments and the basin. The HEC-HMS model, which is a distributed model, was applied only to the entire Kilombero River basin. River discharge, rainfall and potential evaporation data were used as inputs to the appropriate models and it was observed that sometimes the system models performed better than complex hydrological models, especially in large catchments, illustrating the usefulness of using simple black-box models in datascarce situations.  相似文献   

基于智能物联网技术的天津城市积水监测预警系统     

侯天宇  梁好  霍凯  赵敏  陈子煊  张春莉  苑超 《气象研究与应用》2021,(1):85-89

通过智能物联网技术实时获取积水监测实况数据,利用天津市气象精细化格点预报产品和城市自动雨量观测站实况数据,以机器学习、神经网络模型和天津市城市内涝风险等级划分原理为基础,研究基于用户实时位置的城市内涝预报预警技术,研发天津市城市自动化积水监测预警系统。结果表明,该系统具备一定的城市内涝风险监测预警预报能力,并在2018—2020年多次重大天气过程中应用,积水深度预报结果与监测结果基本一致,应用数据表明验证结果良好,系统可以为政府防灾减灾决策、指挥调度提供精准、及时的气象数据支撑。  相似文献   

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image     

Chao LIU  Shu YANG  Di DI  Yuanjian YANG  Chen ZHOU  Xiuqing HU  Byung-Ju SOHN 《大气科学进展》2022,39(12):1994-2007

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.  相似文献   

Climatology of Shear Line and Related Rainstorm over the Southern Yangtze River Valley Based on an Improved Intelligent Identification Method     

刘金卿  陈鹤  徐靖宇 《热带气象学报(英文版)》2022,28(4):413-424

Based on four reanalysis datasets including CMA-RA, ERA5, ERA-Interim, and FNL, this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear. Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley (SYRV) during the summers (June-August) from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm, namely the t-distributed stochastic neighbor embedding method (t-SNE) and the k-means clustering method. The results are as follows: (1) The reproducibility of the 850 hPa wind fields over the SYRV using China’s reanalysis product CMA-RA is superior to that of European and American products including ERA5, ERA-Interim, and FNL. (2) Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities. (3) The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal, but the intensity and the accompanying rainstorm have a clear diurnal variation: they are significantly stronger during daytime than those at nighttime. (4) Half (47%) of the shear lines can cause short-duration rainstorms (≥20 mm (3h)-1 ), and shear line rainstorms account for one-sixth (16%) of the total summer short-duration rainstorms. Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing. (5) Under the influence of stronger water vapor transport and barotropic instability, shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south.  相似文献   

Canny算子对人工标志中心的亚像素精度定位   总被引：10，自引：0，他引：10  

范生宏  黄桂平  陈继华  李广云  周华 《测绘科学技术学报》2006,23(1):76-78

在使用圆型人工标志的计算机视觉检测中,人工标志的识别率和中心定位精度直接影响到检测的整体精度.传统的中心定位算法对人工标志识别率低、中心定位精度差,已不能满足精密检测的要求.文中采用Canny算子对带有圆形人工标志的数字图像进行边缘分割,通过模式识别方法、最小二乘拟合方法计算人工标志中心.该方法解决了标志识别率低的问题,提高了标志图像中心定位精度.其精度可达到亚像素级,能够满足高精度计算机视觉测量的要求.  相似文献   

分数阶傅里叶变换联合支持向量机的建筑物变形预测     

古巍  李倩  陈代果 《大地测量与地球动力学》2022,42(12):1239-1245

提出一种分数阶傅里叶变换(fractional Fourier transformation, FrFT)与支持向量机(support vector machine, SVM)相结合的建筑物变形组合预测模型。首先利用FrFT对变形时间序列进行多尺度分析,将复杂时间序列分解为一系列结构较为简单的子序列;然后利用SVM对每个子序列分别建立预测模型,通过将各个子序列的预测结果进行综合叠加,得到最终预测结果;同时考虑到SVM模型参数选择的难题,提出一种改进果蝇优化算法(improved fruit fly optimization algorithm, IFOA)对其进行全局寻优,提升预测性能。以西南地区某混凝土坝变形实测数据为例开展验证实验,结果表明,本文组合预测模型能够充分挖掘数据中隐含的趋势性和规律性信息,获得较高的预测精度。  相似文献