Estimation of River Bathymetry and Spatial Distribution of Precipitation and Their Uncertainties

Abstract

Hydrological modeling required essential input data such as river bathymetry, discharge, topographic and floodplain elevation values, manning's n values, and precipitation. In this study, river bathymetry and spatial distribution of precipitation were focused because of challenges in their estimation and associated uncertainties. These input data are also important in accurate flood risk and vulnerability assessment. Previous studies developed several models for their estimation using remote sensing and GIS. However, further improvement is required for their accurate estimation. This study will be helpful in accurate spatio-temporal estimation of these two input data. Furthermore, it was suggested the use of machine learning approaches to handle big data obtained from satellite images for further improvement in the spatio-temporal estimation.