• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Advances in space research >A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data
【24h】

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

机译：MSG Seviri数据的每日降雨估计的基于多层的Perceptron和多标准支持向量机的高精度技术

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted 'k' nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.
机译：本文介绍了一种新的多层的Perceptron（MLP）和支持向量机（SVM）的方法，以改善来自Meteosat第二代（MSG）数据的日降雨估计。在这项研究中，首先将沉淀检测并分为两种MLP型号的对流和层状雨，然后使用四种多级SVM算法进行日降雨估计。发达算法的相关光谱和纹理输入特征源自光谱MSG Seviri辐射计通道。使用在北阿尔及利亚的雷达降雨数据集合训练模型。通过记录在北阿尔及利亚的雨量仪网络数据集进行了验证所提出的每日降雨估算技术。因此，计算了几种统计分数，例如相关系数（R），根均方误差（RMSE），平均误差（偏置）和平均误差（MAE）。所提供的结果:( r = 0.97，偏见= 0.31 mm，RMSE = 2.20mm和MAE = 1.07 mm），估计和相应观察到的每日降水之间存在相当令人满意的关系。此外，基于随机森林（RF）和加权'K'最近邻（WKNN）的两种先进技术的结果比较了通过所提出的模型获得的更高的精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号