Study on the relationship between improved short-term precipitation forecast and insurance data for risk evaluation in Southern China
Authors: Zheng Yue , Zhou Ziye , Fang Cong , Liang Jiaxi , Ren Boyang , Lin Jing , Cheng Chunxia , Gu Qingshan 
Published: March 2020

&lt;p&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; Heavy rainfall is one of the most frequent and severe weather hazards in the world which becomeone of the hugest natural risks.&amp;#160; It has been found that during the flood season in South China, high intensive precipitation occurs very frequently due to the impact of east Asian monsoon. &amp;#160;An unexpected and unusual extreme precipitation event could lead to millions or billions worth of damage, wash out vehicles and houses, destroy agricultural fields, and threat people&amp;#8217;s lives.&amp;#160; Determining the linkage between heavy rainfall causes, critical meteorological condition, and impacts can make it easier to classify risk level.&amp;#160; However, due to the insufficiencies of quantitative heavy rainfall related property damages, and low efficient precipitation forecast, the risk evaluation could not be well determined.&amp;#160; Therefore, we employed an improved short-term precipitation forecast based on ensemble deep learning algorithms that can provide more accurate prediction, and apply &amp;#160;25 years of insurance data to aid as proxy for the evaluation of short-term heavy rainfall risks, aiming to trigger in-time precautions and reduce losses.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; The improved short-term precipitation forecast is built based on combination of scale-invariant feature transform (SIFT) algorithm and ensemble model including convolutional neural network (CNN), gradient boosting decision tree (GBDT), and neural network.&amp;#160; The main dataset used includes radar images and station observed precipitation.&amp;#160; The past 1.5 hour radar reflectivity images are measured at 15 times with an interval of 6 minutes, and in 4 different heights from 0.5 km to 3.5 km with an interval of 1 km.&amp;#160; The hourly site precipitation is obtained from ground meteorology stations.&amp;#160; The SIFT is used to calculate cloud trajectory velocity, and the CNN is implemented with features including pinpoint local radar images, spatial-temporal descriptions of the cloud movement and the global description of the cloud pattern.&amp;#160; Weights are assigned to the ensemble model to compute the following 2-3 hours forecasting results.&amp;#160; Additionally, the insurance data include more than 50 thousand records provided on a geography coordinate level for the last 25 years.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; Result shows that the insurance data have a strong correlation with short-term precipitation.&amp;#160; It also indicates that our proposed model of short-term precipitation forecast outperforms only-deep learning-based and traditional optical flow-based methods.&amp;#160; The insurance data could provide a good proxy for describing heavy rainfall damage and to aid to explore the causes and impacts.&amp;#160; This study would greatly assist policy makers, civil protection agencies, and insurance companies to improve emergency systems and response mechanisms.&lt;/p&gt;