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首页> 外文期刊>Neurocomputing >Meta-heuristic Bayesian networks retrieval combined polarization corrected temperature and scattering index for precipitations
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Meta-heuristic Bayesian networks retrieval combined polarization corrected temperature and scattering index for precipitations

机译：亚启发式贝叶斯网络检索结合极化校正温度和散射指数进行降水

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摘要

This paper proposes Bayesian networks (BNs) that combine polarization corrected temperature (PCT) and scattering index (SI) methods to identify rainfall intensity. To learn BN network structures, meta-heuristic techniques including tabu search (TS), simulated annealing (SA) and genetic algorithm (GA) were empirically evaluated and compared for efficiency. The proposed models were applied to the Tanshui river basin in Taiwan. The meteorological data from the Special Sensor Microwave/lmager (SSM/I) of the National Oceanic and Atmospheric Administration (NOAA) comprises seven passive microwave brightness temperatures, and was used to detect rain rates. The data consisted of 71 typhoons affecting the watershed during 2000-2012. A preliminary analysis using simple meta-heuristic BNs identified the main attributes, namely the brightness temperatures of 19,22, 37 and 85 GHz for rainfall retrieval. Based on the preliminary analysis of a simple BN run, the advanced BNs combined with SI and PCT successfully demonstrated improved rain rate retrieval accuracy. To compare the proposed meta-heuristic BNs, the traditional SI method, the SI-based support vector regression model (SI-SVR), and artificial neural network (ANN) were used as benchmarks. The results showed that (1) meta-heuristic BN techniques can be used to identify the vital attributes of the rainfall retrieval problem and their causal relationships and (2) according to a comparison of BNs combined with PCT and SI and artificial intelligence (Al)-based models (SI-SVR and ANN), in heavy, torrential, and pouring rainfall, models of BNs combined with PCT and SI provide a superior retrieval performance than that of AI-based models. Therefore, this study confirms that meta-heuristic BNs combined with PCT and SI is an efficient tool for addressing rainfall retrieval problems.
机译：本文提出了贝叶斯网络（BNs），该网络结合了偏振校正温度（PCT）和散射指数（SI）方法来识别降雨强度。为了学习BN网络结构，对包括禁忌搜索（TS），模拟退火（SA）和遗传算法（GA）在内的元启发式技术进行了经验评估，并进行了效率比较。提出的模型被应用于台湾的淡水河流域。来自国家海洋和大气管理局（NOAA）的特殊传感器微波/成像仪（SSM / I）的气象数据包含七个被动微波亮度温度，并用于检测降雨率。数据包括在2000-2012年期间影响分水岭的71个台风。使用简单的元启发式BN进行的初步分析确定了主要属性，即用于降雨检索的19、22、37和85 GHz的亮温。在对简单的BN运行进行初步分析的基础上，先进的BN与SI和PCT相结合成功地展示了提高的降雨率检索精度。为了比较提议的元启发式BN，将传统的SI方法，基于SI的支持向量回归模型（SI-SVR）和人工神经网络（ANN）用作基准。结果表明：（1）元启发式BN技术可用于识别降雨检索问题的重要属性及其因果关系；（2）根据结合PCT和SI的BN与人工智能（Al）的比较基于BNs的模型（SI-SVR和ANN），在大雨，倾盆和倾盆大雨中，结合PCT和SI的模型提供了比基于AI的模型更好的检索性能。因此，这项研究证实，结合PCT和SI的元启发式BN是解决降雨检索问题的有效工具。

著录项

  • 来源
    《Neurocomputing》 |2014年第20期|71-81|共11页
  • 作者

    Chih-ChiangWei;

  • 作者单位

    Department of Digital Content Designs and Management, Toko University, No. 51, Sec. 2, University Rd., Pu-Tzu City, Chia-Yi County 61363, Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bayesian network; Heuristic technique; Precipitation; Retrieval;

    机译：贝叶斯网络启发式技术;沉淀;恢复;

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