# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
###### Abstract  
Precipitation images play an important role in meteorological forecasting and flood forecasting, but how to characterize precipitation images and conduct rainfall similarity analysis is challenging and meaningful work. This paper proposes a rainfall similarity research method based on deep learning by using precipitation images. The algorithm first extracts regional precipitation, precipitation distribution, and precipitation center of the precipitation images and defines the similarity measures, respectively. Additionally, an ensemble weighting method of Normalized Discounted Cumulative Gain-Improved Particle Swarm Optimization (NDCG-IPSO) is proposed to weigh and fuse the three extracted features as the similarity measure of the precipitation image. During the experiment on similarity search for daily precipitation images in the Jialing River basin, the NDCG@10 of the search results reached 0.964, surpassing other methods. This indicates that the method proposed in this paper can better characterize the spatiotemporal characteristics of the precipitation image, thereby discovering similar rainfall processes and providing new ideas for hydrological forecasting. precipitation image; feature extraction; similarity analysis; multivariate feature fusion; Improved Particle Swarm Optimization 2023 13 4883 10.0000-000  
results and obtain similar stations. [PERSON] [5] proposed a rainfall event similarity analysis model for rainfall forecasting, which evaluated the similarity between two rainfalls from multiple perspectives, such as the quantity similarity, pattern similarity, earth mover's distance, and rainfall spatial distribution similarity; the experimental results showed the similar rainfall analysis method is effective and applicable. [PERSON] [10] developed a new forecasting technique for predicting whether water levels will exceed a 'flood' threshold or not by using deep learning methods based on weather forecast precipitation images, which provided a new idea and reference for extending the flood forecast period. Traditional similarity analysis of rainfall mainly uses time series text data, which cannot effectively represent the spatial distribution of rainfall. Moreover, the existing research on rainfall similarity lacks comprehensive measurement methods for multivariate characteristics of rainfall and is greatly insufficient in terms of interpretation. In the past decade, with the development of information technology, the hydrological departments have accumulated a large amount of spatiotemporal data, and data types have been expanded from traditional time series to semi-structured and unstructured. As is shown in Figure 1, different colors represent different precipitation grade and provide the spatiotemporal distribution information of precipitation in the Jialing River Basin. However, as time and spatial scales accumulate, it becomes more difficult to discover useful knowledge from these increasingly big data. Therefore, utilizing the latest machine learning and artificial intelligence algorithms to carry out feature extraction and fast similarity analysis on the accumulated big data of precipitation images to provide technical support for the identification of similar rainfall-flood processes and flood control is becoming a meaningful and hot research issue [11; 12]. This paper proposes a rainfall similarity research method based on deep learning by using precipitation images. The novelty of this article lies in that the regional precipitation, precipitation distribution, and precipitation center are extracted as the characters of the precipitation image, and then appropriate distance measures for each feature are defined to better characterize the similarity between images. After that, an ensemble weighting method of normalized depreciation cumulative gain-improved particle swarm optimization (NDCG-IPSO) is proposed to weight and fuse the distance measures of three extracted features as the similarity measure for daily precipitation image similarity search.