Deep learning of sea-level variability and flood for coastal city resilience

Abstract

Due to climate change, it is important to study the relationship between floods and sea-level rise in coastal city resilience. In this research sea surface temperature (SST) from MODIS, wind speed, precipitation, and sea-level rise from satellite altimetry are investigated for dynamic sea-level variability. An annual SST increase of 0.1C° is observed around the Gothenburg coast. Also in the middle of the North Sea, an annual increase of about 0.2C° is evident. The annual sea surface height (SSH) trend is 3 mm on the Gothenburg coast. We have a strong positive spatial correlation between SST and SSH near the Gothenburg coast. In the next step, dynamic sea-level variability is predicted with a convolution neural network and long short term memory. Root mean square error of wind speed, precipitation, SST, and mean sea-level forecasts are ±0.84 m/s, ±48.75 mm, ±3.48C° and ±24 mm, respectively. The 5-year trends of mean seal level show a significant increase from 28 mm/year to 46 mm/year in the last 5 year periods and the rate of increase has doubled. In the final step, the water rise of 5–10 m in Gothenburg city was simulated, and in the worst scenario, more than 50 % of the city will be damaged.