]> 2026-04-30T06:50:12-06:00 Model:DeltaClassification 3002 en 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 No not possible 5 Boston 1 No not possible No but possible No not possible Single Processor United States no https://zenodo.org/record/3926763#.Xvz5mpNKiu4 v1.0 10.5281/zenodo.3926763 Proof of concept was applied for the Ganges-Brahmaputra delta system 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. 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 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 Methods has been applied to data set of 100's of individual delta islands derived from Landsat satellite data (30-60m resolution). 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. 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 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. Mapview at a given time As is, no updates are provided 2020 No 2019 2020 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 Mariela 1 ASCII Perignon 0.17 No n/a Coastal DeltaClassification As code machine learning delta landforms Tool geometry islands active geomorphic processes extracting information nature and spatial spatial extent extent of active active geomorphic geomorphic processes processes across deltas unsupervised machine learning machine learning algorithm machine learning ensemble unsupervised machine remotely sensed imagery two-step ensemble unsupervised spatially continuous zones delta landforms deltaclassification 2 Geometry classification of delta islands ASCII maps false 02101 false Compatible with Python 2.7, not python 3 BSD or MIT X11 Python n/a Through CSDMS repository https://github.com/csdms-contrib/DeltaClassification 2D Landscape-Scale 2017 Massachusetts Linux Mac OS Model developer false 2020-04-10T15:14:25Z 2458950.1350116 2020-09-17T02:18:32Z 2459109.5962037 DeltaClassification 3002 0 plainlist 1 [[:Model:DeltaClassification]] DeltaClassification# QUERYae5167a63758eac2ad89988e303ba84b 3002 1 plainlist 5 [[User:+]] [[First name member::Mariela]] [[Last name member::Perignon]] DeltaClassification# QUERYbda2c1fbc3400b9e259693497b5aae58 3002 1 ul 5 [[Model:+]] [[First name::Mariela]] [[Last name::Perignon]] DeltaClassification# QUERY2812b418a19df24329d024d2b7d10247