In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no–leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.
During the Great East Japan Earthquake in 2011, real-time estimate of the earthquake’s magnitude was quite low, and consequently, the first report about the tsunami also understated its severity. To solve this issue, some proposed a massive overhaul of Japan’s offshore tsunami observation networks and methods to predict tsunamis in real time. In this study, we built a database containing 3,967 scenarios of tsunamis caused by earthquakes with hypocenters along the Nankai Trough, and tested a tsunami prediction method that uses this database along with offshore tsunami observation networks. Thus, we found that an uneven distribution of observation points had a negative effect on predictive accuracy. We then used simulated annealing to select the observation points to be used at each observation site and found that the predictive accuracy improved while using a few selected observation points compared to using every point.
After the Nankai earthquake in 1946, the resultant flooding lasted for a long time, because seawater remained on land after the tsunami in Kochi city. Large-scale flooding occurred in Ishinomaki city immediately after the Great East Japan Earthquake in 2011. Long-term flooding may hamper disaster responses such as rescue and recovery activities. This paper studied the risks of long-term flooding after the Nankai earthquake in Tokushima city based on a paleographical survey and numerical analysis. The paleographical survey identified statements such as “seawater sometimes flowed onto the land at the full tide,” suggesting occurrences of long-term flooding after previous Nankai earthquakes. The numerical analysis separately calculated values inside and outside the levee.
The tsunami waveforms outside the analysis area obtained by tsunami numerical simulation was used as the boundary condition of the inland flow modeling, that is water was introduced inside the levee when the tsunami water level exceeded the upper end of the levee. The two layers of ground surface and the drain were defined to calculate the flow, including water exchange between the two layers, and the water was drained forcefully outside the levee using a drainage pump. The possibility of long-term flooding in the analysis area is suggested when a large-scale earthquake occurs in the Nankai trough.
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