DOI: 10.25338/b8vg83
title: Channel types predictions for the South Fork Eel River basin

description: Hydrologic and geomorphic classifications have gained traction in response
 to the increasing need for basin-wide water resources management.
 Regardless of the selected classification scheme, an open scientific
 challenge is how to extend information from limited field sites to
 classify tens of thousands to millions of channel reaches across a basin.
 To address this spatial scaling challenge, we leveraged machine learning
 to predict reach-scale geomorphic channel types using publicly available
 geospatial data.
description: A bottom-up machine learning approach selects the most accurate
 and stable model among ~96,000 and derives the relationship between 147
 predictors and labels corresponding to regional channel types in a
 three-tiered framework which: (i) define a tractable problem; assess model
 performance (ii) in statistical learning; and (iii) in prediction. In the
 present application to the South Fork Eel River catchment (California,
 USA), the developed framework selects a Random Forest model to predict 7
 channel types previously determined from 96 field-surveys over 8,022 200-m
 stream interval. Performance in statistical learning is
 high with a 88% median cross-validation accuracy and a 0.98 mean
 multiclass Area Under Curve value. Furthermore, the predictions coherently
 capture the expected geomorphic organization of the landscape. As main
 metric of uncertainty, we include for each stream-segment the entropy
 calculated from the posterior probabilities output from the machine
 learning algorithm. For completeness, evenness and richness are also
 reported. The predictions included in this dataset
 corresponds to an aggregated version of the output from the machine
 learning framework. Each initial 200-m stream interval was aggregated by
 using their COMIDs from the National Hydrography Dataset corresponding to
 the most common identifier used by stake-holders. For each resulting NHD
 stream line, the 200-m scale probabilities associated to each channel
 types are summed and normalized to sum to one.
description: Format: shapefile (.shp) Spatial
 reference: Projection: California Albers
 Datum: NAD83 Units: meters
 Attributes: COMID:
 Common identifier of the NHD feature      FDATE:
 Feature Currency Date      RESOLUTION: Always
 "Medium" GNIS_ID: Geographic Names
 Information System ID for the value in GNIS_Name   
 GNIS_NAME: Feature Name from the Geographic Names Information
 System LENGTHKM:  Feature length in
 kilometers REACHCODE: Reach Code assigned to
 feature FLOWDIR: Flow direction is “WithDigitized” or
 “Uninitialized”   WBAREACOMI: ComID of an NHD
 polygonal water feature through which an NHD “Artificial Path” flowline
 flows FTYPE: NHD Feature Type    
 FCODE: Numeric codes for various feature attributes in the
 NHDFCode lookup table     SHAPE_LENG: Feature length
 in decimal degrees ENABLED: Always "True" 
   GNIS_NBR: Internal field for data
 processing group: most probable channel types  
   shannon: Shannon's entropy calculated from the
 posterior probabilities   richness: Richness
 calculated from posterior probabilities evenness:
 Evenness calculated from posterior probabilities
 SFE01: posterior probability for channel type SFE01 (i.e.
 membership)      SFE02: posterior probability for
 channel type SFE02 (i.e. membership)      SFE03:
 posterior probability for channel type SFE03 (i.e. membership)   
   SFE04: posterior probability for channel type SFE04
 (i.e. membership)      SFE05: posterior probability
 for channel type SFE05 (i.e. membership)      SFE06:
 posterior probability for channel type SFE06 (i.e. membership)   
   SFE07: posterior probability for channel type SFE07
 (i.e. membership)  
   The seven channel types are:
 SFE01: Confined high width-to-depth, gravel cobble,
 rifle-pool SFE02: Unconfined, gravel,
 riffle-pool SFE03: Confined, gravel-cobble,
 bed-undulating SFE04: Confined, high width-to-depth,
 gravel-boulder, uniform SFE05: Confined, low
 width-to-depth, gravel-cobble, uniform SFE06:
 Partly-confined, gravel-cobble, uniform SFE07:
 Confined, high-gradient, cobble-boulder, step-pool/cascade