Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
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通过 ANN 机器学习在稀疏测量区域中使用融合数据绘制区域降水图
DOI：
10.3390/app9112294
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发表时间：
2019-06
影响因子：
2.7
通讯作者：
Xia Ting
中科院分区：
文献类型：
--
作者：
Xu Guoyin;Wang Zhongjing;Xia Ting
针对无测或稀疏测区水资源评估，利用遥感数据、有限监测数据以及基于机器学习的监测数据与遥感数据融合，对面域降水量进行对比评价。采用人工神经网络（ANN）模型融合遥感降水和地面降水。采用相关系数、均方根偏差、相对偏差和一致性原则评价遥感降水的可靠性。以柴达木盆地为例，热带降水测量卫星（TRMM）-3B42RT和TRMM-3B43原始遥感降水产品与实测降水相比，精度分别为0.61、72.25毫米、36.51%、27%和0.70、64.24毫米、31.63%、32%。校正后的TRMM-3B42RT和TRMM-3B43精度分别提高到0.89、37.51 mm、–0.08%、41%和0.91、34.22 mm、0.11%、42%，表明以高程、经度和纬度为降水主要影响因素的数据挖掘是高效有效的。对柴达木盆地面降水量的评价表明，根据仪表资料、修正的TRMM-3B42RT和修正的TRMM-3B43，年平均降水量分别为104.34毫米、186.01毫米和174.76毫米。结果表明，基于稀疏规范降水数据和融合遥感数据的面降水存在很大差异。
Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data.
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影响因子：
6.4
作者：
S. Bohnenstengel;K. H. Schluenzen;F. Beyrich
通讯作者：
S. Bohnenstengel;K. H. Schluenzen;F. Beyrich
DOI：
--
发表时间：
2014
期刊：
Journal of China Hydrology
影响因子：
--
作者：
Wang Xiaoji
通讯作者：
Wang Xiaoji
影响因子：
5.4
作者：
HSU, KL;GUPTA, HV;SOROOSHIAN, S
通讯作者：
SOROOSHIAN, S
DOI：
10.23919/tma.2018.8506558
发表时间：
2018-06
期刊：
2018 Network Traffic Measurement and Analysis Conference (TMA)
影响因子：
--
作者：
Giuseppe Aceto;D. Ciuonzo;Antonio Montieri;A. Pescapé
通讯作者：
Giuseppe Aceto;D. Ciuonzo;Antonio Montieri;A. Pescapé
DOI：
10.1007/978-3-540-77655-0_6
发表时间：
2008
期刊：
--
影响因子：
--
作者：
A. Hou;G. Skofronick-Jackson;C. Kummerow;James Marshall Shepherd
通讯作者：
A. Hou;G. Skofronick-Jackson;C. Kummerow;James Marshall Shepherd