Development of Heavy Rain Damage Prediction Technique Based on Optimization and Ensemble Method

• Published in: KSCE journal of civil engineering Vol. 27; no. 5; pp. 2313 - 2326
• Main Authors: Kim, Donghyun, Han, Heechan, Lee, Haneul, Kim, Hung Soo, Kim, Jongsung
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01.05.2023; Springer Nature B.V; 대한토목학회
• Subjects: Atmospheric precipitations; Civil Engineering; Damage; Datasets; Disaster management; Disasters; Early warning systems; Emergency communications systems; Emergency preparedness; Emergency warning programs; Engineering; Flood management; Geotechnical Engineering & Applied Earth Sciences; Industrial Pollution Prevention; Objective function; Optimization; Precipitation; Predictions; Rain; Rainfall; Training; Water Resources and Hydrologic Engineering; 토목공학; Heavy rain advisory; Heavy rain damage prediction technique; Ensemble concept; Optimization method; Early warning system
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-023-2099-0

Korea’s early warning system for flood disaster management is starting preparations for flood response by the heavy rain advisory (HRA) of the Korea Meteorological Administration (KMA). However, the HRA criterion has a critical limitation in that it considers only consistent rainfall patterns (e.g. 60 mm/3 hrs or 110 mm/12 hrs) without considering the characteristics of heavy rain damage in the region. To address this problem, the present study proposes a heavy rain damage prediction technique based on optimization and ensemble method. To predict damage as accurately as possible, fifteen rainfall variables according to the duration and magnitude are considered. The dataset is divided into a training dataset (70%) and a test dataset (30%) by random extraction. An optimal threshold that the damage can be occurred is derived for each region via optimization method of the training dataset. The area under the receiver operating characteristic (AUROC) curve, F1 score, and F2 score are each considered as objective function, and the F2 score was selected because it is more effective in terms of disaster management. In addition, the method is designed to predict damage probabilistically by applying the ensemble concept. This novel method is defined as a heavy rain damage prediction technique (HDPT). Finally, the HDPT is evaluated using the test dataset and by comparison with the results from the HRA data, and the F2 score of the HDPT is shown to be about 10% higher than that of the HRA. Thus, the proposed methodology is expected to be more effective than the current HRA method for the early warning system and for disaster management.