Model:DeltaClassification

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DeltaClassification

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Summary Contact Technical specs In/Output Process Testing Other Component info Also known as DeltaClassification Model type Tool Model part of larger framework Note on status model Date note status model Incorporated models or components: Spatial dimensions 2D Spatial extent Landscape-Scale Model domain Coastal One-line model description Geometry classification of delta islands Extended model description This tool provides a method for extracting information on the nature and spatial extent of active geomorphic processes across deltas from the geometry of islands and the channels around them using machine learning. The method consists of a two-step ensemble unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on morphological metrics computed on remotely sensed imagery Keywords: machine learning, delta landforms, Name Mariela Perignon Type of contact Model developer Institute / Organization Postal address 1 Postal address 2 Town / City Boston Postal code 02101 State Massachusetts Country United States Email address mperignon@gmail.com Phone Fax Supported platforms Linux, Mac OS Other platform Programming language Python Other program language Compatible with Python 2.7, not python 3 Code optimized Single Processor Multiple processors implemented Nr of distributed processors Nr of shared processors Start year development 2017 Does model development still take place? No If above answer is no, provide end year model development 2019 Code development status As is, no updates are provided When did you indicate the 'code development status'? 2020 Model availability As code Source code availability (Or provide future intension) Through CSDMS repository Source web address Source csdms web address https://github.com/csdms-contrib/DeltaClassification Program license type BSD or MIT X11 Program license type other Memory requirements n/a Typical run time n/a Describe input parameters Geometric parameters on delta shapes derived from satellite data. To run this code, the following shape files are required: • network shapefile, containing the river network extracted from satellite imagery • island shapefile, containing the land masses or islands of the delta • patch shapefile, containing the outline of channels Input format ASCII Other input format Describe output parameters classification of groups of similar zones within a deltasystem code blocks that: • loads in the shapefiles • calculate the parameters for the network that both surround and drain the islands • calculate the base metrics (e.g. perimeter, area, solidity, aspect ratio...) • calculates maximum distance from the island center to the nearest water body • estimates minimum, average and maximum widths of all network channels • evaluates the fractal dimension of each delta island • creates shapefiles based on the metrics calculated earlier in the code • saves all metrics to an output file • generates PCA and GeoSOM results from the island and channel metrics • plots the U-matrix and dendrogram based on the GeoSOM results Output format ASCII Other output format maps Pre-processing software needed? No Describe pre-processing software Post-processing software needed? No Describe post-processing software Visualization software needed? No If above answer is yes Other visualization software Describe processes represented by the model Tool is used to regionalize a study area into zones with 'common physical characteristics' with the underlying aim of differentiating areas of influence of various physical processes. Regionalization attempts to aggregate spatial units or observations into clusters based on spatial continuity as well as attribute similarity. Geometry metrics are derived from satellite data analysis and include a.o. island area, island aspect ratio, island fractal dimension, and surrounding channel metric, channel width, channel sinousity, number of outflow channels, convexity. Describe key physical parameters and equations The key methods used are: 1) Feature normalization and principal component analysis 2) Spatial clustering using GEOSOM algorithm 3) Hierarchical agglomerative clustering to built nested clusters Describe length scale and resolution constraints Methods has been applied to data set of 100's of individual delta islands derived from Landsat satellite data (30-60m resolution). Describe time scale and resolution constraints Mapview at a given time Describe any numerical limitations and issues n/a Limitations on the method do occur when delta systems consist of only a few islands, then the input dataset of geometric parameters becomes too small for the machine learning methods. Describe available calibration data sets Proof of concept was applied for the Ganges-Brahmaputra delta system Upload calibration data sets if available: Describe available test data sets Test data was slightly updated from a published dataset by Passalacqua, P., Lanzoni, S., Paola, C., and Rinaldo, A.: Geomorphic signatures of deltaic processes and vegetation: The Ganges- Brahmaputra-Jamuna case study, Journal of Geophysical Research: Earth Surface, 118, 1838–1849, 2013. Upload test data sets if available: Describe ideal data for testing Do you have current or future plans for collaborating with other researchers? no Is there a manual available? No Upload manual if available: Model website if any Model forum / discussion board Comments Requirements To run these codes, you will need the following software: • Python 2.7 or earlier (not compatible with Python 3) The following Python packages are also required: • matplotlib • scipy • numpy • cPickle • osgeo • fiona • shapely • utilities • sklearn • seaborn • clusterpy • itertools • pandas • pysal • collections What input is required? To run this code, the following shape files are required: • network shapefile, containing the river network extracted from satellite imagery • island shapefile, containing the land masses or islands of the delta • patch shapefile, containing the outline of channels What does the code do? The file all.ipynb contains codes run the analysis. From start to finish, the Jupyter Notebook contains code blocks that: • loads in the shapefiles • calculate the parameters for the network that both surround and drain the islands • calculate the base metrics (e.g. perimeter, area, solidity, aspect ratio...) • calculates maximum distance from the island center to the nearest water body • estimates minimum, average and maximum widths of all network channels • evaluates the fractal dimension of each delta island • creates shapefiles based on the metrics calculated earlier in the code • saves all metrics to an output file • generates PCA and GeoSOM results from the island and channel metrics • plots the U-matrix and dendrogram based on the GeoSOM results This part will be filled out by CSDMS staff OpenMI compliant No not possible BMI compliant No not possible WMT component No not possible PyMT component No but possible Is this a data component DOI model 10.5281/zenodo.3926763 For model version v1.0 Year version submitted 2020 Link to file https://zenodo.org/record/3926763#.Xvz5mpNKiu4 Can be coupled with:

Model info Authors Mariela Perignon Source code View in CSDMS GitHub repository DOI Download DeltaClassification version: v1.0 Doi: 10.5281/zenodo.3926763 Model citations Nr. of publications: 2 Total citations: 5 h-index: 1 m-quotient: 0.17 QR-code Link to this page Other models by author AnugaSed DeltaClassification PyDeltaRCM

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Nr. of publications:	2
Total citations:	5
h-index:	1
m-quotient:	0.17

Featured publication(s)	Year	Model described	Type of Reference	Citations
Perignon, Mariela; Adams, Jordan; Overeem, Irina; Passalacqua, Paola; 2020. Dominant process zones in a mixed fluvial-tidal delta are morphologically distinct. . (View/edit entry)	2020	DeltaClassification	Model overview	5
Perignon, M.C.; 2020. csdms-contrib/DeltaClassification: First release of DeltaClassification (Version v1.0). , , . 10.5281/zenodo.3926763 (View/edit entry)	2020	DeltaClassification	Source code ref.	0
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