Adaptive hydrological flow field modeling based on water body extraction and surface information

Hydrological flow characteristic is one of the prime indicators for assessing flood. It plays a major part in determining drainage capability of the affected basin and also in the subsequent simulation and rainfall-runoff prediction. Thus far, flow directions were typically derived from terrain data which for flat landscapes are obscured by other man-made structures, hence undermining the practical potential. In the absence (or diminutive) of terrain slopes, water passages have a more pronounced effect on flow directions than elevations. This paper, therefore, presents detailed analyses and implementation of hydrological flow modeling from satellite and topographic images. Herein, gradual assignment based on support vector machine was applied to modified normalized difference water index and a digital surface model, in order to ensure reliable water labeling while suppressing modality-inherited artifacts and noise. Gradient vector flow was subsequently employed to reconstruct the flow field. Experiments comparing the proposed scheme with conventional water boundary delineation and flow reconstruction were presented. Respective assessments revealed its advantage over the generic stream burning. Specifically, it could extract water body from studied areas with 98.70% precision, 99.83% recall, 98.76% accuracy, and 99.26% F-measure. The correlations between resultant flows and those obtained from the stream burning were as high as 0.80 +/- 0.04 (p <= 0.01 in all resolutions). (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)