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

Abstract

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.

Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework

“…Under the framework, the 330 sites over SEC were assigned into training and validation sites from which the training and validation data were extracted, respectively. With reference to previous studies, the ratio of the training data to the validation data was set to be 10:1 [36,38,52]. That is, 30 out of 330 sites were selected randomly as validation sites, and the remaining 300 sites were set as training sites (in Figures 1b and 2).…”

“…Ma et al [36] derived the merged rainfall data over the Tibet Plateau by adopting the dynamic Bayesian model averaging scheme, and also evaluated the assimilated precipitation data in four seasons and at different elevations over Tibet. The artificial neural networks (ANNs) have also been used to assimilate multi-source precipitation data including satellite-based, gauge-based and radar datasets in different regions [37][38][39]. There are also other nonparametric methods, such as the general regression neural network (GRNN) [40] and Bayesian nonparametric general regression [41].…”

Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

“…Xu et al [21] in their paper entitled "Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region" showed an efficient method to map areal precipitation with the data fused from the remote-sensing precipitation acquired from Tropical Precipitation Measurement Satellite (TRMM) product and ground gauge precipitation using the ANN method.…”