Application and evaluation of CNN-LSTM classification regression based multi-source precipitation data fusion model in water resources research in Qinghai Province

Abstract

Qinghai Province is located in the Qinghai-Tibet Plateau region, with complex and diverse topography and sparse precipitation stations, which makes it difficult to obtain reliable precipitation data. This study proposes a classification and regression model based on a deep learning algorithm, which combines a convolutional neural network (CNN) and a long short-term memory neural network (LSTM), with the CNN extracting the spatial features of multi-source data, the LSTM capturing their temporal dependencies. The regression results are used to determine whether rainfall is occurring and to further calibrate the non-rainfall component of the precipitation forecast results. ERA5, IMERG, CHIRPS and DEM were selected as feature data and rain gauge data as label data. The findings indicate that the proposed CNN-LSTM classification regression model (CLCR) is superior to other models (CNN, CNN-LSTM, LSTM). The Kling-Gupta efficiency (KGE) of the data fused using CLCR was 0.66, which was significantly better than that of the raw rainfall data (0.53, -0.36, 0.34) and other models (0.58, 0.65, 0.63). CLCR also showed more performance in daily precipitation detection than other models and raw precipitation data, with Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR) 0.61, 0.25 and 0.76 respectively. This study generated a high-precision daily rainfall dataset with a precision of 0.01° resolution for 2013-2017 in Qinghai Province, which provides reliable data support for hydrological studies in Qinghai Province.