# Study of Precipitation Forecast Based on Deep Belief Networks
## 2 Related Work  
For the requirement of high quality and high resolution precipitation products, the National Oceanic and Atmospheric Administration (NOAA), National Severe Storms Laboratory (NSSL), and the National Weather Service (NWS) Hydrology Development Office jointly developed the NMQ plan (The National Mosaic and Multisensor QPE Project), developed real-time quantitative precipitation estimations and introduced all kinds of high resolution (QPE) products [2,3]. Meanwhile, the free MPing software (Version 2.0) developed is used to collect meteorological data of the public's participation in the perception, collects the meteorological information around the public's location, and transmits it to the server through the MPing software installed in smart mobile devices to assist the dual-polarization radar data. The weather forecasting method has achieved long-term development in recent years, with a lot of scholars establishing some prediction models about precipitation forecasting, such as the ARIMA (Autoregressive Integrating Moving Average) model [4, 5, 6, 7], Markov model [8, 9, 10], gray theory-based prediction model [11], and so on. These studies have contributed to the development of the precipitation forecast. However, there are some shortcomings that should be further studied. The Markov model and gray model-based forecasting model are more suitable for the exponential growth of rainfall. The prediction error of extremum is larger in the ARIMA model. Deep learning is a novel machine learning method proposed in the field of artificial intelligence in recent years. Deep learning can be an effective big data processing method by training big data, mining and capturing the deep connection between big data to improve the classification and prediction accuracy. In addition, the deep learning model training is faster, and it shows better performance than the general growth of the method with the increase of training samples. The weather forecast model based on deep learning is better equipped to overcome the shortcomings of the existing forecast methods. Due to the achievements of deep learning algorithms in various fields, more and more people have tried to use deep learning algorithms in the field of weather forecasting, and some progress has been made. Researchers have done a lot of important work in this area. For example, [PERSON] demonstrated the potential of identifying the structure and parameters of three-layer feed forward Artificial Neural Network models (ANN), and it provided a better representation of the rainfall-runoff relationship of the medium-size Leaf River basin near Collins, Mississippi, than the linear ARMAX (autoregressive moving average with exogenous inputs) and the conceptual SAC-SMA (Sacramento soil moisture accounting) model [12]. [PERSON] proposed that the deep neural network (DNN) model may granulate the features of the raw weather data layer by layer to process massive volumes of weather data [13]. [PERSON] and [PERSON] evaluated the effectiveness of three data-driven models, the artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN), by using the standard precipitation index for forecasting drought conditions in the Awash River Basin of Ethiopia [14]. [PERSON] proposed a long term rainfall forecasting model using the integrated wavelet and neuro-fuzzy long term rainfall forecasting model [15]. The other study has shown the applicability of an ensemble of artificial neural networks and learning paradigms for weather forecasting in southern Saskatchewan, Canada [16]. [PERSON] forecasted annual precipitation based on a non-linear autoregressive neural network (NARNN) and non-linear inputbility of an ensemble of artificial neural networks with historical precipitation data, and the results showed that the accuracy of the NARNNX (non-linear autoregressive neural network with exogenous input) was better than that of the NARNN and NIO (non-linear input-output), based on values of r [17].