Many cities in Taiwan are affected by the flood after the typhoon, especially Tainan City. The loss assessment which impacts to economies and societies was the big issue for government. This study offers a GIS-based automatic model to analyze the direct loss and risk impacts caused by the flood in a residential area in the urban.
The replacement cost models are used in the automatic GIS model that has been made. The 3Di flood simulation data i.e. simulated water depth was used in this research based on real typhoon events in 2016, Megi Typhoon and 2017, Haitang Typhoon. The flood loss assessment identifies the impact from the average of water depth in the residential area. The highest water depth along 8-hour simulation during the typhoon event was used for estimating the maximum loss of residential building. The flood vulnerable area was mapped using four vulnerability factors: distance from the river, distance to the fire station, elevation and people density. The Analytical Hierarchy Process (AHP) as Multi-Criteria Decision Making (MCDM) method was used for getting weight factor for each vulnerability factors. The flood risk map was derived from vulnerability map and flood hazard map to show the potential loss area of the flood disaster.
The replacement cost model was applied by our survey in the impacted area. The result showed that the larger loss occurred in 2016 Megi Typhoon event than 2017 Haitang Typhoon. The loss was found in 2016 Megi Typhoon event amounting to NT$ 257,760,212 whereas the loss resulted in NT$ 137,362,931 in 2017 Haitang Typhoon. Result was indicated that the replacement cost model played an important role for direct loss assessment of flood disaster. In addition, the different flood resolution data also provide the significant various effects of water depth for the total loss. The difference loss value due to the different flood resolution data showed a value of less than 5% - 15% of the difference loss value for lower data resolution. This result indicated that the high resolution flood case offered the less loss than that from low resolution of flood data.

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