利用一种新的神经网络模型识别点状地图符号   总被引：1，自引：0，他引：1  

黄文骞 《武汉大学学报(信息科学版)》1996,(1)

着重讨论了用一种新的神经网络模型识别点状地图符号的过程，主要包括网络的结构特点和学习算法以及学习训练过程，并验证了用该网络进行点状地图符号识别的有效性。  相似文献   

A minimalistic approach for evapotranspiration estimation using the Prophet model     

A. T. M. Sakiur Rahman  Takahiro Hosono  Ozgur Kisi  Boateng Dennis  A. H. M. Rahmatullah Imon 《水文科学杂志》2020,65(12):1994-2006

ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R² > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R² > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.  相似文献   

星载合成孔径雷达海洋遥感与大数据     

李晓明  黄冰清  贾童  覃婷婷 《南京气象学院学报》2020,12(2):191-203

星载合成孔径雷达以其全天候、全天时、不受云雨影响的工作特性在空间对海观测中起到了重要作用，又以其高空间分辨率、多极化、多成像模式的特点展示了其在海洋动力要素反演和海洋多尺度动力过程研究中独特的魅力.起步于20世纪70年代末的星载合成孔径雷达技术，迎来了发展的"黄金时期"，大数据和机器学习又赋予了星载合成孔径雷达海洋遥感更强大的生命力.本文首先阐述了星载合成孔径雷达大数据的5"V"特性，进而以高分辨率海面风场反演、海洋内波中尺度动力过程观测两类典型案例，阐述了大数据、机器学习等现代信息科学技术与卫星海洋遥感结合，实现海洋环境参数高精度反演和海洋动力过程科学深层次认知的研究.最后，展望了星载合成孔径雷达海洋遥感与大数据的发展前景.  相似文献   

Multiobjective analysis of chaotic dynamic systems with sparse learning machines     

Abedalrazq F. Khalil  Mac McKeeMariush Kemblowski  Tirusew AsefaLuis Bastidas 《Advances in water resources》2006

Sparse learning machines provide a viable framework for modeling chaotic time-series systems. A powerful state-space reconstruction methodology using both support vector machines (SVM) and relevance vector machines (RVM) within a multiobjective optimization framework is presented in this paper. The utility and practicality of the proposed approaches have been demonstrated on the time series of the Great Salt Lake (GSL) biweekly volumes from 1848 to 2004. A comparison of the two methods is made based on their predictive power and robustness. The reconstruction of the dynamics of the Great Salt Lake volume time series is attained using the most relevant feature subset of the training data. In this paper, efforts are also made to assess the uncertainty and robustness of the machines in learning and forecasting as a function of model structure, model parameters, and bootstrapping samples. The resulting model will normally have a structure, including parameterization, that suits the information content of the available data, and can be used to develop time series forecasts for multiple lead times ranging from two weeks to several months.  相似文献   

基于CART集成学习的城市不透水层百分比遥感估算   总被引：1，自引：0，他引：1  

朱海军  江利明  林珲  杨立民 《武汉大学学报(信息科学版)》2007,32(12):1099-1102,1106

利用Landsat ETM^＋遥感数据，提出了一种基于CART集成学习的ISP遥感亚像元估算方法，将Boosting重采样技术引入CART分析中，用于提高ISP估算的精度。实验结果表明，该方法的ISP估算性能优于传统的单一CART学习算法，从ETM^＋影像中估算的ISP值与真实值之间的相关系数达到0．91，平均偏差为11．16％。  相似文献   

投影寻踪学习网络的遥感影像分类   总被引：2，自引：0，他引：2  

严勇  李清泉  孙久运 《武汉大学学报(信息科学版)》2007,32(10):876-879

采用投影寻踪(projection pursuit,PP)学习网络方法建立了一种新的遥感影像分类模型。该方法结合了统计学中投影寻踪算法节点函数灵活的非参数估计特点和人工神经网络的自学习功能,具有简捷的网络结构和良好的鲁棒性能。利用苏州市TM影像进行了分类实验,将分类结果与BP神经网络和最大似然法的分类结果相比较,投影寻踪学习网络的分类精度较高,具有一定的实用性。  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   

成人英语学习动机行为调查与教学对策研究   总被引：3，自引：0，他引：3  

陈金中 《成都信息工程学院学报》2004,19(2):292-296

动机是激励人们行动的心理动因.英语学习动机是推动英语学习的内部动因,成人英语学习动机是复杂的.了解他们的英语学习动机特点有利于教师改进教学手段和策略,激发与培养其学习的主动性和积极性,发挥其学习潜能,从而提高英语学习的效果.运用定量研究的方法,在年轻干部学员中进行学习动机调查,分析了其学习动机的特点,并提出成人英语教学的对策.  相似文献   

һ�ּ��㲹����С�������򻯲��������Ż�����     

丁士俊  朱留洋  姜卫平 《大地测量与地球动力学》2015,35(1):115-117

??????????????С????????У??????Ч????????????????????????????????μ??????С????????????????????彻??????L??????????????????????????????????????????????????????????????????????????Ч???????  相似文献   

基于高清工业相机的盾构隧道裂缝智能识别算法分析     

李梓豪  唐超 《测绘通报》2021,(8):83-87,101

裂缝一直是隧道病害的重点检测对象,但传统人工巡检仅能通过肉眼发现后记录,人工识别精准度与效率完全取决于个人经验判断,无信息化手段辅助,作业效率识别精度亟待提升。针对以上问题,本文借助高清工业相机成像分辨率高、采集速度快等特点,将高清工业相机部署于轨道车上获取隧道表面裂缝病害信息,大幅提高了隧道裂缝识别效率,将识别精度提升至0.2 mm,同时融入优化的Cascade R-CNN算法,在有监督情况下训练隧道裂缝样本,最终实现了隧道裂缝病害的高效提取,同时研发了一套包含硬件数据采集、数据处理软件、数据管理平台的裂缝病害识别路线,真正意义上破除了识别慢、精度低、靠经验、难管理的技术壁垒。  相似文献