ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 46 Issue 3 > 2020,46(3):393-402. DOI:10.7519/j.issn.1000-0526.2020.03.010 Prev Next
Assessing Model of Casualty Loss in Rainstorms Based on Random Forest and Its Application
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Abstract:
Based on historic casualty loss records of rainstorm that occurred at county level in Guangxi from 2009 to 2017, seven factors were selected as explanatory variables by comprehensively considering the trigger factors, disaster formative environment and exposure units, and the prediction model of casualty loss caused by rainstorms was built up by using random forest algorithms. The refined grid precipitation analysis and forecast products were used to drive the model to predict loss of life. The results showed that the classification accuracies are both above 90% in training and testing samples. Disastertriggering factors (precipitation) are the most significant explanatory variables. The importances of these precipitation variables in turn are the anomaly percentage of accumulated precipitation over the previous 10 days, the maximum daily precipitation, the maximum hourly precipitation and the frequency of shorttime severe rainfall. By applying the intelligent grid precipitation products, several rainstorm processes in Guangxi in recent two years were used to verify the model, showing that prediction accuracies are above 70%.
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