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Improving Climate Resilience Through Smart Agriculture
Evan Coopersmith, Murugesu Sivapalan, Barbara Minsker,
University of Illinois Urbana-Champaign
Craig Wenzel, Brian Gilmore, Larry Hendrickson,
John Deere Technology Innovation Center
Field Readiness Predictions
Input: Rainfall
Output: Qualitative Field Readiness (rated 1-5)
The model is trained on these data to predict:
‘READY’ or ‘NOT READY’
This tractor is a very
expensive waste of
machine resources…
This tractor could become
stuck and/or leave ruts.
Active Soil Moisture Sensor Poor/Missing Data
EBI_NE
EBI_NE
EBI_CEN
EBI_SE
Jordan'sSampling
Case Study Test Site:
South Farms, Urbana, IL
(Left) The South Farms, Urbana-Champaign, IL
(Right) Soil Sensor Locations
Wetness/Dryness conditions were
recorded by a John Deere intern
during the summer of 2010.
Algorithms were constructed to
predict these conditions remotely
using Illinois Climate Network
(ICN) data along with public
Nexrad radar data.
Field Readiness Workflow
Comparison of Algorithm Performance:
Classification Trees, KNN, and Boosted Perceptrons
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ClassificationTree KNN BoostedPerceptron
% O
f D
ays
Cla
ssif
ied
Corr
ectl
y
Agreement of Human and Computer
KNN Results, Summer 2010
All testing examples for which no rain has fallen for 3+ days have been removed
All errors fall within margin of error (the error band crosses the dotted line)
All errors occur on days for which readiness is “2” or “3”
(Borderline qualitative assessments)
The Next Step: Understanding the problem on a national scale, using soil
moisture as our proxy for “readiness”
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Diagnostic Soil Moisture Equation by Pan et al (2012)
(Overlaid with a machine learning model for error-correction)
Assumes that soil moisture losses due to evapo-transpiration, drainage, etc can be
estimated by an annual sinusoidal function. This requires three parameters…
y = sin(x) y = sin(x) + v {} {v} y = αsin(x) + v {v, α} y = αsin(x-h) + v {v, α, h}
Diagnostic Soil Moisture Equation:
6 Parameters are required
Once these first three parameters are fit via a genetic algorithm, three final parameters are fit via
a 2nd genetic algorithm based on observed soil moisture values and the chosen loss function.
{v, α, h} {v, α, h, ϴre}
Residual Soil Moisture Effective Porosity Soil Drainage Constant
{v, α, h, ϴre , Фe} {v, α, h, ϴre , Фe , C4}
Machine Learning Overlay:
Improvement Beyond the Published Literature
In New Mexico, the model tends to perform very well. The
ML algorithm in this place makes a tremendous difference.
ρ = 0.860 ρ = 0.917
“So…you can model soil moisture at these
locations where you have soil moisture
sensors…but what about everywhere else?”
(We need a national system for hydrologic classification)
Hydrologic Classification of Watersheds: 4 Features
Seasonality (is rainfall consistent year-round or variable?)
Aridity (how does total annual precipitation compare with annual potential evap.?)
Maximum Precipitation Timing (when is rainfall the highest?)
Maximum Runoff Timing (when is streamflow the highest?)
Level One (2 splits)
Level Two (4 splits)
The first split divides the country via seasonality, dividing
the less seasonal east from the more seasonal west.
Exceptions are found in the Rockies and in Florida.
The second split
divides the eastern half
of the country via
rainfall timing and the
Western half via aridity.
Recursive Splitting: Building a “Classification Tree”
Splitting continues until clusters of
similar hydrologic behavior emerge.
Classes - 428 MOPEX Catchments
IACJ
IAF
IAQ
IHD
IHM
ISCB
ISCJ
ISQJ
ITC
ITF
LBMH
LBMS
LJ
LPC
LPM
LPQ
LWC
XACJ
XADB
XHD
XSC
XSMB
XTM
XVM
The Finished Tree, All 24 Classes
LWC
LJ
LPC
ITC
ISQJ
LPM
Only 6 classes describe over 77% of
the 428 catchments in the database.
What About Non-Stationarity?
428 MOPEX Catchments:
Divided into Pre & Post-1975 data
Second Split (Before & After)
High Seasonality & Arid (Red), High Seasonality & Humid (Yellow), Low Seasonality
& Early Precip (Dark Blue), Low Seasonality & Late Precip (Dark Blue)
Before 1980 After 1980
-130 -120 -110 -100 -90 -80 -7025
30
35
40
45
50
0
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0.5
0.6
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0.8
0.9
1
-130 -120 -110 -100 -90 -80 -7025
30
35
40
45
50
0
0.1
0.2
0.3
0.4
0.5
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The Midwest, Before & After 1980:
Later Peak Runoff
Higher Low-Flows
The Pacific Northwest, Before & After 1980:
Diminished
Snowpacks
Diminished
Snowpacks
Unchanged
Winter Runoff
The Rocky Mountains, Before & After 1980:
Smaller
Snowpacks
Smaller
Snowpacks
Steadier Rainfall
Steadier Rainfall
The Southeast, Before & After 1980:
Systemic Drying Systemic Drying
“You can model soil moisture where you have soil moisture sensors…
You can tell me if two locations are similar…
You can select more recent years to define classification similarity…
Does this mean you can calibrate at one location and apply the
parameters at another ‘similar’ site?
(Yes!)
The red samples consist of (x,y) pairs where x & y are
from unrelated classes on the classification tree.
The yellow samples consist of (x,y) pairs where x & y
are not from the same class, but from classes that are
different only by one “split” (feature) on the tree.
The green samples consist of (x,y) pairs where x & y are
from the same class.
Making Predictions in Champaign…without
any prior knowledge of soil moisture
The Complete Process (A Summary)
Calibration
(Pan et al, 2012)
Classes - 428 MOPEX Catchments
IACJ
IAF
IAQ
IHD
IHM
ISCB
ISCJ
ISQJ
ITC
ITF
LBMH
LBMS
LJ
LPC
LPM
LPQ
LWC
XACJ
XADB
XHD
XSC
XSMB
XTM
XVM
(Coopersmith et al, 2012)
Generalization
Via Classification
Cross-
Validation
Topography Nexrad Data,
From Champaign
Calibration of parameters
using a SCAN site of the
same class (Arkansas)
Machine
Learning
Future
Potential Applications (Disaggregation)
SMAP data
(kilometer scale)
Precipitation radar data
(kilometer scale)
LiDAR data
(meter scale)