# Presentation: 192963

## Discovering Communicable Scientific Knowledge from Spatio-Temporal Data

- Mark Schwabacher
- NASA Ames Research Center
- Computational Sciences Division
- mark.schwabacher@arc.nasa.gov
- http://ic-www.arc.nasa.gov/people/schwabacher/
- Joint work with Pat Langley and Jeff Shrager (ISLE) and Chris Potter, Steve Klooster, Lisy Torregrosa, and Vanessa Brooks (NASA Earth Science)

## Outline

- Description of Earth science problem
- Choice of representation and algorithm
- Results
- Visualizations
- Discovery of an error in the data
- Future Work

## Earth Science Problem

- The Normalized Difference Vegetation Index (NDVI) is a measure of vegetation across the globe derived from satellite data
- NDVI is used in various Earth-science models
- Unfortunately, NDVI is only available for the years since 1983, when a satellite with these sensors was launched
- We would like to predict NDVI at a point on the globe from ground-based climate variables representing temperature, precipitation, and moisture

## Choice of Representation

- For scientific applications, the learned models should be
- Understandable
- Communicable

## Representation used by scientists

- Our Earth Science collaborators had built the following model with an “if” statement to select between two linear models, one for warmer locations and one for cooler locations:
- if GDD<3000 then
-   ln(NDVI) = 0.715 ln(GDD) + 0.377 ln(PPT) – 0.448
- if GDD>= 3000 then
-   NDVI = 189.89 AMI + 44.02 ln(PPT) + 227.99

## Choice of Algorithm

- We selected regression rules as a generalization of the Earth scientists’ representation
- We selected Cubist to learn themhttp://www.rulequest.com

## First Results

|  | r | # rules |
| --- | --- | --- |
| Cubist | 0.91 | 41 |
| Linear regression with expert-selected cut | 0.86 | 2 |

- Cubist produced better accuracy, but model was hard to understand.

## Varying the Cubist minimumrule cover parameter

## 2-rule Cubist model

- if PPT <= 25.457 then
-    NDVI = -3.225 + 7.07 PPT + 0.0521 CDD - 84 AMI+ 0.4 ln(PPT) + 0.0001 GDD
- if PPT > 25.457 then
-    NDVI = 386.3 + 316 AMI + 0.0294 GDD - 0.99 PPT + 0.2 ln(PPT)

## Visualization #1:Cubist model in one variable

## Visualization #2: Activity of Cubist Rules

## Visualization #2:Error of Cubist model

## Testing the model across years

- We trained Cubist using one year’s data
- We tested the resulting model on other years’ data
- If it transfers, it’s useful for Earth scientists
- If it sometimes doesn’t transfer, that could point to a scientific discovery

## Discovery of an error in the data

- Cross-validate 1985

- Train 1984, test 1985

## Related Work

- Regression trees: Breiman *et al*’s CART (1984)
- Classification applied to Earth science: Brodley & Friedl (1999); Ester, Kriegel, & Xu (1996)
- Visualizing classes on map: Brodley & Friedl (1999); Smyth, Ghil, & Ide (1999)
- Detecting and correcting faulty class labels in data: John (1995); Brodley and Friedl (1999)
- Detecting and correcting calibration problems in remote-sensing systems using predefined model: Chen (1997)

## Future Work

- Cubist/NDVI work
- Incorporate time explicitly
- Include other variables (e.g. elevation)
- Test understandability
- Other work
- Improve CASA model (next talk)
- Implement an interactive system that lets scientists direct high-level search for improved ecosystem models

## Lessons Learned

- We’ve identified three problems that arise in scientific applications of ML, and proposed initial solutions:
**Communicability**: Use the same representation as the scientists.
**Understandability****:** When using spatial data, spatially visualize the model’s errors and the activity of its components.
**Quantitative errors****: **When using time-series data, quantitative errors can be identified by testing a model trained on one time period against data from other time periods.