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Article

Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region

by 1, 1,2,3,* and 4
1
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2
State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing 100084, China
3
State Key Lab of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
4
China Renewable Energy Engineering Institute, Beijing 100120, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(11), 2294; https://doi.org/10.3390/app9112294
Received: 30 March 2019 / Revised: 18 May 2019 / Accepted: 28 May 2019 / Published: 4 June 2019
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. View Full-Text
Keywords: Qaidam Basin; remote sensing; TRMM; artificial neural network Qaidam Basin; remote sensing; TRMM; artificial neural network
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MDPI and ACS Style

Xu, G.; Wang, Z.; Xia, T. Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region. Appl. Sci. 2019, 9, 2294. https://doi.org/10.3390/app9112294

AMA Style

Xu G, Wang Z, Xia T. Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region. Applied Sciences. 2019; 9(11):2294. https://doi.org/10.3390/app9112294

Chicago/Turabian Style

Xu, Guoyin, Zhongjing Wang, and Ting Xia. 2019. "Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region" Applied Sciences 9, no. 11: 2294. https://doi.org/10.3390/app9112294

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