Flooding has been increasing since 2004 in Japan due to localized heavy rainfall and geographical conditions. Determining areas vulnerable to flooding as one element of flood hazard maps related to disaster management for urban development is necessary. This research integrated Remote Sensing data, the Geography Information System (GIS) method and Analytical Hierarchy Process (AHP) calculation to determine the physical flood-vulnerable area in Okazaki City. We developed this research by applying data from the Geospatial Information Authority of Japan (GSI) to generate the slope map and drainage density; AMEDAS (Automated Meteorological Data Acquisition System) from the Japan Meteorological Agency (JMA) to generate the rainfall data; Soil map from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) data; and Sentinel-2 imagery to generate the land cover map. We applied the AHP calculation for weighting pairwise the parameters by comparing five iterations of the normalized matrix. We utilized the spatial analysis tool in ArcGIS to run the pairwise comparison to adjudicate the distribution of flooding according to the AHP procedure. The percentage of relative weight was slope (43%), drainage density (20%), rainfall intensity (17%), then both infiltration rate and land cover (10%). The consistency value was reasonable: consistency index (CI-0.007) and consistency ratio (CR-0.6%). We generated high accuracy for flood vulnerability prediction; 0.88 for Probability of Detection (POD), 0.28 for Probability of False Detection (POFD), 0.44 for Critical Success Index (CSI), 1.9 for Bias, and 95 of Area under Curve (AUC). The flood vulnerability was matched to the flood inundation survey of Okazaki City in August 2008 and indicated an excellent Relative Operating Characteristic (ROC).

No abstract

No abstract

This paper addresses the time history finite element analysis of rock-structure interaction. Modeled is not only the lateral energy dissipation, but also the interaction between the far field and the numerical model itself. This is accomplished by a preliminary analysis of the far field as a shear beam (for lateral excitation), and then velocities and displacements are transferred to the model as nodal forces through damping and stiffness matrices respectively. Details of the finite element implementation are given, along with an extensive series of simulations comparing this method, with the one of Lysmer for both 2D and 3D models. The model is derived from the principle of virtual work, and its implementation does not require any modification of existing finite element codes, only clever pre and postprocessing of results are needed.

In this paper, the Global Satellite Mapping of Precipitation Moving Vector with Kalman filter (GSMaP_MVK) was evaluated and corrected at daily time scales with a spatial resolution of 0.1˚ latitude/longitude. The reference data came from thirtyfour rain gauges on Kyushu Island, Japan. This study focused on the GSMaP_MVK's ability to detect heavy rainfall patterns that may lead to flooding. Statistical analysis was used to evaluate the GSMaP_MVK data both quantitatively and qualitatively. The statistical analysis included the relative bias (B), the mean error (E), the Nash-Sutcliffe coefficient (CNS), the Root Mean Square Error (RMSE) and the correlation coefficient (r). In addition, Generalized Additive Models (GAMs) were used to conduct GSMaP_MVK data correction. The results of these analyses indicate that GSMaP_MVK data have lower values than observed data and may be significantly underestimated during heavy rainfall. By applying GAM to bias correction, GS-MaP_MVK's ability to detect heavy rainfall was improved. In addition, GAM for bias correction could effectively be applied for significant underestimates of GSMaP_ MVK (i.e., bias of more than 55%). GAM is a new approach to predict rainfall amount for flood and landslide monitoring of satellite base precipitation, especially in areas where rain gauge data are limited.