RiverBottomInterpolator

This code allows interpolating partial river bathymetry points to the entire domain, taking into account the anisotropy of longitudinal and lateral bottom vaiations.

Getting Started

• Change directory into one of the example folders

cd Examples/CDD

• Run the script

python3 ../../riverBottomInterpolator.py

• Inpect the input and output files geojson files using QGIS or other GIS software.

Parameters

All the parameters for the interpolation are read from the parameters.json file in the current folder. For example:

{
    # [Input] Name of the geojson file containing the cross sections that define
    # the river outline
    "cross_section_file_name": "cross_sections.geojson",

    # [Input] Name of the numeric field that defines the order of the cross
    # sections; can be float or integer
    "cross_section_order_field_name": "s",

    # [Input] Name of the geojson file containing the bottom elevation surveyed
    # points
    "points_file_name": "depth_points.geojson",

    # [Input] Name of the numeric field that defines the elevation of the bottom
    # on the surveyed point
    "points_z_field_name": "z",

    # [Input] Rate of lateral variability to longitudinal variability
    "anisotropy": 20.0,
    
    # [Input] Interpolation method
    "interpolation_method": "IDW",
   
    # [Input] Number of neighbours for the IDW method
    "IDW_num_neighbours": 12,
   
     # [Input] Power of distance for the IDW method
    "IDW_power": 1,

    # [Input] Desired average output resolution, in meters
    "dx": 5.0,

    # [Ouput] Name of the geojson file containing the river outline
    "river_outline_file_name": "river_outline.geojson",

    # [Ouput] Name of the geojson file containing the interpolated points
    "results_file_name": "interpolated_points.geojson"
}

All the input geojson files need to have the same projected coordinate system.

How it works

The algorithm works a following:

• It reads a number of cross section segments that define the extent, and orientation of the river reach.

• It then creates a conformal grid of points following that outlines, with the desired resolution

• The conformal grid acts as a map between a cartesian rectangular space and the geometry in geographical coordinates.

• The surveyed points are transformed into the cartesian rectangular space.

• Interpolation is calculated on the cartesian rectangular space. Currently only Inverse Distance Weighted interpolation is supported, but it's easy to adapt any other interpolation technique, such a Krigging.

• Points on the cartesian space are exported with their original geographical coordinates

Requirements

• Python 3
• Numpy

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Author

Nicolás Diego Badano (https://www.linkedin.com/in/nicolas-diego-badano/)