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Research Reports
Anticipatory governance support : a study on climate environmental modeling for transitioning to data science
In 2019, a record flood in 53 years in Venice, Italy submerged 80% of the city, and Australia experienced a catastrophic forest fire due to abnormally high temperatures and severe drought. As such, abnormal weather conditions have been frequently observed around the world due to climate change such as heat wave, cold wave, flood and drought. In particular, according to the Abnormal Climate Report 2018 jointly published by related ministries in South Korea, the damage from climate change around the world was mostly concentrated on water disaster, and the UN Department of Economic and Social Affairs (DESA) predicts that many cities around the world will face water-related dangers such as flood and drought going forward.
Recently, studies have been actively conducted to predict such abnormal climate changes and minimize the damage resulting from them using information and communications technology (ICT) in the Fourth Industrial Revolution and big data analysis methodologies such as data mining and machine learning. This study examined data for damage occurrence analysis focusing on flood damage caused by localized heavy rain and typhoon among abnormal climate changes, and implemented a flood damage prediction system for Korea by utilizing climate change scenarios. In addition, data mining techniques were reviewed for analyzing and predicting the damage from various natural disasters and a case study was conducted on flooding and landslide to identify the availability of the techniques.
First, text mining was performed to identify trends in flood damage and determine data ite