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In the broad field of sensors and 3D information retrieval, bathymetric reconstruction from side-scan sonar imaging is associated with unique technical hurdles. Neural Networks have recently led to promising new solutions in this field, but the available methods tend to be complex and data-intensive in a way typically making their use in a riverine environment impossible. Throughout our work, we have focused on simplifying the problem-handling and treating compatibility with a riverine environment as priority. In our work, Long Short-Term Memory proved to be effective in a surprisingly simple form. Combined with traditional post-processing techniques in the GIS environment, like median filtered focal statistics, our workflow ultimately results in ∼0.259 m median of error on the evaluation dataset of the Dráva River.

Digital Elevation Models (DEMs) act as an essential input to hydraulic models for the simulation of flood hazard maps. Coarser resolution DEMs are subject to higher levels of uncertainty, particularly in capturing river‐bed bathymetry. The principal aim of the present study was to assess the potential use of freely available DEMs and the 1D and 2D HEC‐RAS modeling approaches for flood mapping in four different types of river morphology. The DEM created from direct river survey (Surveyed DEM) was used as a benchmark to assess three remote sensing‐based DEMs (i.e., ALOS, SRTM, and ASTER). The results indicated that ALOS is more accurate. However, the regeneration of main‐channel bathymetry was poor for floodplain‐meandering and braided river reaches. The DEMs were then corrected by optimizing the number and layout of representative sections. A total of 144 tests were carried out. The results indicated that the flood‐mapping index F of ALOS increased from 86% to 91%. The flood mapping with a 1D model was sufficient in straight reaches (F‐index 84%). In floodplain‐meandering rivers, a 2D model had to be used to reach an F‐index up to 81%. The responses of both 1D and 2D models were almost identical in the case of wide‐braided rivers. [ABSTRACT FROM AUTHOR]

Topography represented in the form of Digital Elevation Models (DEMs) has profound applications in hydrological modeling. DEMs can be generated from several sources including satellite products, contours, survey data, and LiDAR, each with their own merits and demerits. Where high resolution, accuracy, and spatial extent are concerned, it is often found that a DEM from one source alone is not able to represent the topography of the target area with full accuracy. Upon comparing different DEMs, it was found that most were able to successfully represent mountainous regions but failed to represent flat deltaic regions. Therefore, in this research with Bago River basin, Myanmar as a study area, a new methodology to combine multiple sources of data with different data types is developed. The inputs are: (a) a 10 m DEM, developed using contour data, point elevation data, and UTM topographic maps; (b) a 5 m Digital Surface Model (DSM) acquired by the Advanced Land Observation Satellite (ALOS); and (c) 168 sets of multiple-point elevation data representing a cross-sectional survey along the Bago River and the Bago-Sittaung canal. The output is a 10 m resolution Enhanced DEM (EnDEM) which is able to preserve the merits of all the input data, i.e., upper mountainous region, lower flat deltaic basin, and the river bathymetry. This paper provides a novel approach to DEM integration and burning of the river cross-section onto the DEM. [ABSTRACT FROM AUTHOR]

Bathymetric measurements play an important role in assessing the sedimentation rate, deposition of pollutants, erosion rate, or monitoring of morphological changes in a river, lake, or accumulation basin. In order to create a coherent and continuous digital elevation model (DEM) of a river bed, various data interpolation methods are used, especially when single-beam bathymetric measurements do not cover the entire area and when there are areas which are not measured. Interpolation methods are based on numerical models applied to natural landscapes (e.g., meandering river) by taking into account various morphometric and morphologies and a wide range of scales. Obviously, each interpolation method, used in standard or customised form, yields different results. This study aims at testing four interpolation methods in order to determine the most appropriate method which will give an accurate description of the riverbed, based on single-beam bathymetric measurements. The four interpolation methods selected in the present research are: inverse distance weighting (IDW), radial basis function (RBF) with completely regularized spline (CRS) which uses deterministic interpolation, simple kriging (KRG) which is a geo-statistical method, and Topo to Raster (TopoR), a particular method specifically designed for creating continuous surfaces from various elevation points, contour, or polygon data, suitable for creating surfaces for hydrologic analysis. Digital elevation models (DEM's) were statistically analyzed and precision and errors were evaluated. The single-beam bathymetric measurements were made on the Siret River, between 0 and 35 km. To check and validate the methods, the experiment was repeated for five randomly selected cross-sections in a 1500 m section of the river. The results were then compared with the data extracted from each elevation model generated with each of the four interpolation methods. Our results show that: 1) TopoR is the most accurate technique, and 2) the two deterministic methods give large errors in bank areas, for the entire river channel and for the particular cross-sections. [ABSTRACT FROM AUTHOR]

Topographic data plays an essential role in hydraulic modelling of floods. A high‐precision digital elevation model (DEM) including river bathymetry (bed topography) is required. DEMs can be derived from such various data sources as ground surveying or remote sensing techniques. This study is focused on (a) the DEM error that results from the inability to scan the morphology of the channel using remote sensing methods, and (b) assessment of its impact on the results of a one‐dimensional (1D) hydraulic model. DEMs produced by remote sensing techniques were tested in combination with ground surveying and by software‐updated remote sensing data. Differences in riverbed representation and thalweg position were evaluated. The 1D hydraulic model HEC−RAS was chosen to determine the impact of various DEM sources on the hydraulic quantities (water surface elevation, inundation area). The study was carried out on a reach of the River Vltava (Czech Republic). The best results were achieved by DEMs that combined remote sensing data with ground mapping. Good results also were obtained using software‐updated remote sensing data. Neglecting of cross‐sectional area in remote sensing data has an important impact on the results of hydrodynamic models. [ABSTRACT FROM AUTHOR]

An appropriate digital elevation model (DEM) is required for purposes of hydrodynamic modelling of floods. Such a DEM describes a river's bathymetry (bed topography) as well as its surrounding area. Extensive measurements for creating accurate bathymetry are time-consuming and expensive. Mathematical modelling can provide an alternative way for representing river bathymetry. This study explores new possibilities in mathematical depiction of river bathymetry. A new bathymetric model (Bathy-supp) is proposed, and the model's ability to represent actual bathymetry is assessed. Three statistical methods for the determination of model parameters were evaluated. The best results were achieved by the random forest (RF) method. A two-dimensional (2D) hydrodynamic model was used to evaluate the influence of the Bathy-supp model on the hydrodynamic modelling results. Also presented is a comparison of the proposed model with another state-of-the-art bathymetric model. The study was carried out on a reach of the Otava River in the Czech Republic. The results show that the proposed model's ability to represent river bathymetry exceeds that of his current competitor. Use of the bathymetric model may have a significant impact on improving the hydrodynamic model results. [ABSTRACT FROM AUTHOR]

The main purpose of the present research is to develop software for reconstruction of the river bed on the basis of sparse cross-section measurements. The tools prepared should support the process of hydrodynamic model preparation for simulation of river flow. Considering the formats of available data and the requirements of modern modeling techniques, the prepared software is fully integrated with the GIS environment. The scripting language Python 2.7 implemented in ArcGIS 10.5.1 was chosen for this purpose. Two study cases were selected to validate and test the prepared procedures. These are stream reaches in Poland. The first is located on the Warta river, and the second on the Ner river. The data necessary for the whole procedure are: a digital elevation model, measurements of the cross-sections in the form of points, and two polyline layers representing an arbitrary river centerline and river banks. In the presented research the concept of a channel-oriented coordinate system is applied. The elevations are linearly interpolated along the longitudinal and transversal directions. The interpolation along the channel is implemented in three computational schemes linking different tools available in ArcGIS and ArcToolbox. A simplified comparison of memory usage and computational time is presented. The scheme linking longitudinal and spatial interpolation algorithms seems to be the most advantageous. [ABSTRACT FROM AUTHOR]