Improving Climate Resilience Through Smart Agriculture

Transcription

1 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

2 Field Readiness Predictions Input: Rainfall Output: Qualitative Field Readiness (rated 1-5) This tractor could become stuck and/or leave ruts. The model is trained on these data to predict: READY or NOT READY This tractor is a very expensive waste of machine resources

3 Case Study Test Site: South Farms, Urbana, IL Wetness/Dryness conditions were recorded by a John Deere intern during the summer of Algorithms were constructed to predict these conditions remotely using Illinois Climate Network (ICN) data along with public Nexrad radar data. Active Soil Moisture Sensor EBI_NE Poor/Missing Data EBI_NE (Left) The South Farms, Urbana-Champaign, IL (Right) Soil Sensor Locations EBI_SE Jordan's Sampling EBI_CEN

4 Field Readiness Workflow

5 % Of Days Classified Correctly Comparison of Algorithm Performance: Classification Trees, KNN, and Boosted Perceptrons Agreement of Human and Computer 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% ClassificationTree KNN BoostedPerceptron

6 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)

7 The Next Step: Understanding the problem on a national scale, using soil moisture as our proxy for readiness???????

8 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 y = = y αsin(x-h) αsin(x) = + + v v {v, {v, {v} {} α, α} h}

9 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 2 nd genetic algorithm based on observed soil moisture values and the chosen loss function. {v, {v, α, {v, α, h, {v, h, ϴα, h, re ϴ, re h} ϴФ, re Ф}, e C} 4 } Residual Soil Moisture Effective Porosity Soil Drainage Constant

10 Machine Learning Overlay: Improvement Beyond the Published Literature ρ = In New Mexico, the model tends to perform very well. The ML algorithm in this place makes a tremendous difference.

11 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)

12 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?)

13 Recursive Splitting: Building a Classification Tree The first split divides the country via seasonality, dividing the less seasonal east from the more seasonal west. Exceptions are found in the Level Rockies and One Two in Florida. (4 (2 splits) The second split divides the eastern half of the country via rainfall timing and the Western half via aridity. Splitting continues until clusters of similar hydrologic behavior emerge.

14 The Finished Tree, All 24 Classes ITC LPM LPC ISQJ LJ LWC Classes MOPEX Catchments IACJ IHM ITC LJ LWC XSC IAF IAQ IHD ISCB ISCJ ISQJ ITF LBMH LBMS LPC LPM LPQ XACJ XADB XHD XSMB XTM XVM Only 6 classes describe over 77% of the 428 catchments in the database.

15 What About Non-Stationarity? 428 MOPEX Catchments: Divided into Pre & Post-1975 data

16 Second Split (Before & After) High Seasonality & Arid (Red), High Seasonality & Humid (Yellow), Low Seasonality & Early Precip (Dark Blue), Low Seasonality & Late Precip (Dark Blue) 50 Before After

17 Later Peak Runoff The Midwest, Before & After 1980: Higher Low-Flows

18 Diminished Snowpacks Unchanged Winter Runoff The Pacific Northwest, Before & After 1980: Diminished Snowpacks

19 Smaller Snowpacks Steadier Rainfall The Rocky Mountains, Before & After 1980: Steadier Rainfall Smaller Snowpacks

20 The Southeast, Before & After 1980: Systemic Drying Systemic Drying

21 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!)

22 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.

23 The Complete Process (A Summary) Calibration of parameters using a SCAN site of the same class (Arkansas) Machine Learning (Pan et al, 2012) Calibration Making Predictions in Champaign without any prior knowledge of soil moisture Nexrad Data, From Champaign Topography Future Generalization Via Classification Cross- Validation Classes MOPEX Catchments IACJ IAF IHM ISCB ITC ITF LJ LPC LWC XACJ XSC XSMB (Coopersmith et al, 2012) IAQ ISCJ LBMH LPM XADB XTM IHD ISQJ LBMS LPQ XHD XVM

24 Potential Applications (Disaggregation) SMAP data (kilometer scale) Precipitation radar data (kilometer scale) LiDAR data (meter scale)