# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 1 Introduction  
Precipitation is one of the essential links in the water cycle process and varies significantly whether it is spatial or temporal [1; 2]. Traditionally, the measurement of precipitation is based on a ground gauge station such as a hydrometric station or meteorological station. The gauge precipitation is identified in terms of both effectiveness and accuracy due to its direct measurement. The spatial distribution of precipitation is mostly interpolated from the gauged data. However, the accuracy of interpolation in the sparse and uneven gauged area is generally not reliable [3]. Therefore, the fusion of remote sensing data and gauged data for evaluation has become a challenging topic [4; 5; 6; 7; 8]. There are many high-resolution rainfall products at both the global and regional scales which have been released successively [9; 10], such as the Global Precipitation Climate Program (GPCP), Global Satellite Mapping Precipitation Program (GSMaP), Tropical Precipitation Measurement Satellite (TRMM) and Global Precipitation Measurement (GPM) [11; 12; 13]. Many remote sensing precipitation products [14; 15; 16; 17] are widely used to compensate for the shortage of gauged data areas [18; 19; 20; 21]. However, the remote sensing precipitation production is not highly reliable due to its indirect observation which needs adjusting and evaluation [22; 23; 24; 25]. There are many achievements published about the evaluation of remote sensing precipitation products. [26; 27; 28; 29; 30]. The precision evaluation index of remote sensing precipitation products mainly includes a correlation coefficient, determination coefficient, scatter slope, fuzzy comprehensive score, etc. [31; 32; 33; 34]. For those remote sensing precipitation products with low precision, it is necessary to be corrected. The most used method is machine learning, such as the classification and regression tree (CART), random forest (RF), multi-factor data mining set correction, etc. [18; 35]. An artificial neural network (ANN) is a powerful machine learning algorithm with a complex network structure formed by the interconnection of a large number of processing units (neurons) [36]. It is an information processing system based on imitating the structure and function of the brain neural network [37]. The theory of an ANN has made significant progress in pattern recognition [38; 39], automatic control, signal processing [40], assistant decision-making, artificial intelligence [41], networking and healthcare [42; 43; 44; 45]. It has been successfully introduced into the field of hydrology and water resources [46; 47]. There are many publications on TRMM remote sensing precipitation products for applicability in specific areas [48; 49; 50]. [PERSON] et al. [51] found that the accuracy of both the total and the monthly precipitation of TRMM in the Amazon basin are high enough. [PERSON] et al. [52] validated the accuracy of TRMM precipitation products and found it has a high accuracy in Sichuan and Chongqing in China on seasonal and monthly scales. [PERSON] et al. [53] analyzed TRMM precipitation products with the observation data of meteorological stations in the Tianshan Mountains and its surrounding areas, and the results showed that the TRMM products had good applicability. A large number of research results showed that the accuracy of TRMM precipitation products was higher on monthly and annual scales [54; 55], which could be used for analyzing the dynamic variability of a long-time precipitation sequence [56]. However, [PERSON] et al. [57] evaluated the daily precipitation products of TRMM in the Irrawaddy River basin and found that the remote sensing precipitation and the measured values had a high correlation but a large deviation. [PERSON] et al. [58] evaluated the TRMM precipitation in the southern part of the Qinghai-Tibet Plateau by gauged data from high-density rainfall stations and found that TRMM overestimated the amount of light rains.