基于机器学习模型的淮河流域中长期径流预报研究

准确可靠的中长期径流预报是支撑水资源科学调配、提高水资源利用效率的关键。本研究采用AdaBoost模型(AdB)、随机森林模型(RF)和支持向量机模型(SVM)进行淮河流域王家坝和蚌埠站当年11月至次年10月共12个月的中长期径流预报研究。采用置换准确度重要性度量法从130项气象-气候因子及前期降雨/流量构建的1 562个因子变量中筛选出影响各月径流的关键因子,构建了基于AdB、RF和SVM模型的各月径流预报模型,模型参数采用随机搜索技术并结合交叉验证方式确定。采用变幅误差合格率和等级(五级)预报合格率指标对模型的预报精度进行了评估。变幅误差合格率指标表明,王家坝12个月的平均合格率分别为99.8%(AdB)、96.6%(RF)和95.9%(SVM),蚌埠站分别为100%(AdB)、94.8%(RF)和93.8%(SVM);等级预报合格率指标表明,王家坝12个月的平均合格率分别为79.0%(AdB)、76.4%(RF)和79.9%(SVM),蚌埠站分别为81.0%(AdB)、75.6%(RF)和76.6%(SVM)。模型均具有较好的预报效果,但RF和SVM模型对于高流量值的预报存在偏低现象,AdB模型整体上优于RF和SVM模型。     

改进支持向量机算法无人机影像信息精确提取

支持向量机(SVM)算法作为一种成功应用于大多数遥感影像的分类方法,虽然具有较高的提取精度,但是针对分类中仅仅采用单一参数,严重依赖于参数选择的不足,该文基于AdaBoost算法提出一种改进的SVM分类方法。该方法采用选择径向基函数作为核函数的SVM算法作为AdaBoost的弱分类器,实现了核参数的自适应调整。实验结果证明,该方法可以达到精确提取无人机影像信息的目的。     

AdaBoost算法在街景图像数据隐私保护中的应用

针对街景图像数据提出一种图像数据隐私保护技术,研究了基于Ada Boost的人脸检测技术,针对街景图特点,引入肤色检测和眼睛定位技术来提高街景图像的检测速度和精确程度。同时,研究了基于Ada Boost的车牌检测技术,并结合街景图中车辆位置与车牌颜色特征来提高检测的精确程度。最后讨论了对人脸与车牌视觉内容的保护技术。     

基于AdaBoost算法的城镇建设用地遥感动态提取研究

利用2007年的上海地区CBERS影像,使用AdaBoost组合分类器,将基于影像地物光谱信息、纹理结构信息和基于决策树分类器的提取方法组合,以提高分类精度。实验结果显示,AdaBoost算法在提高城镇建设用地的分类精度上显著有效。     

基于AdaBoost算法的遥感影像水体信息提取

AdaBoost算法利用每个特征构造一个简单分类器,然后将简单分类器进行训练组合成一个强分类器。算法能够充分利用每个分类器的优势并避免其劣势,得到一个最佳判别,达到提高分类精度的目的。本文利用TM影像,将影像各波段灰度、水体指数和谱间关系特征相结合,构成提取水体的强分类器,实现水体提取。实验结果表明,算法能够非常有效地、高精度地提取水体信息。     

A Boosted Genetic Fuzzy Classifier for land cover classification of remote sensing imagery

A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. After the rule generation stage, a genetic tuning stage is employed, aiming at improving the cooperation among the fuzzy rules, thus increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. Comparative results with well-known classifiers, commonly employed in remote sensing tasks, indicate that the proposed system is able to handle multi-dimensional feature spaces more efficiently, effectively exploiting information from different feature sources.     

基于AdaBoost算法的多层砖房震害影响因素评估

多层砖房是最常用的结构形式之一,对其进行震害预测尤为重要,选择合适的影响因素是震害预测中最重要的环节之一。本文基于AdaBoost算法对多层砖房震害影响因素进行评估,给出影响因素重要度排序,并将结果与灰色关联分析法进行比较。研究发现结构合理性、地震峰值加速度、砖墙面积率和场地条件对多层砖房震害影响最大,因此在多层砖房震害预测中应优先考虑这4个因素,并在结构设计建造过程中予以足够重视。     

Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization

ABSTRACT

Filtering is one of the key steps for Digital Elevation Model (DEM) generation from airborne Light Detection and Ranging (LiDAR) data. Machine-learning-based filters have emerged as a class of filtering algorithms in recent years. Most existing studies mainly focus on feature generation due to limited available features a point cloud possesses. More than 30 features have been described in the existing literature. But most generated features are based on geometric information of points. Several redundant and irrelevant features may not necessarily improve the filtering accuracy. Hence, this paper proposes a feature-selection method using minimal-Redundancy-Maximal-Relevance (mRMR) combined with Parzen window optimization to deal with both discrete and continuous features. An optimal/suboptimal feature subset is constructed for machine-learning filters in various landscapes. Experimental results based on AdaBoost show that height-related features, particularly height itself, are of the greatest significance in both urban and rural scenes. Moreover, different subsets can be selected from the datasets of the two landscapes by our feature-selection strategy, which increases the data relevance for describing each geographical landscape. This study provides guidelines for the selection of optimal/suboptimal features for point cloud filtering based on machine-learning algorithms.     

基于AdaBoost组合学习方法的岩爆分类预测研究

针对岩爆等级划分问题,考虑了岩爆灾害发生的多种主要影响因素,采用新的数据挖掘方法AdaBoost（即Adaptive Boosting）的组合学习方法,结合流行的人工神经网络BP算法,构建了集成神经网络AdaBoost-ANN（简称AB-ANN）的岩爆等级多分类预测模型。该模型克服了单一弱分类器的不稳定性,提高了分类器精度,实验结果表明,预测的结果与实际值比较吻合,证明了该方法的可行性。