Streamlining Project

Delivery Through Predictive

Modeling

Idaho National Laboratory and

GeoEngineers, Inc

ESRI International

User Conference

GeoEngineers – Applied Technology Business Unit

14 offices nationwide

GIS/CAD/IT Experts

Subject matter expertise in earth sciences and engineering

We are focused on: GIS/IT Strategic planning

Application development

System Integration and Implementation

GIS Analysis and Spatial Modeling

CAD/GIS Cartography

CAD Technologies and Integration

From one-off projects to Repeatable Solutions

Patterns of needs

Leveraging technology frameworks (Esri, Equis, etc)

Responsible Engagements

Consistent approach to our Services

Keeping the best interests of our clients in mind

Stop wasting everyone’s time

GeoEngineers Services

Applied Technology - Solutions

GeoPredictWatershed Prioritization

Environmental Data Viewer

Mobile

Avian

Protection

Idaho National Laboratory (INL) Overview

INL is a US Department of Energy (US DOE) facility

History of reactor development, fuel reprocessing and nuclear research

Yearly monitoring requirements for the radionuclide Cs-137

Cs-137 is a fission product distributed in/on site soils

GeoEngineers:

Joanne Markert –Project Manager

Tonya Kauhi – GeoProcessing Lead

Stan Miller - GeoStatistics

Rob Smith – Technical Architect

Mike Mills - Programmer

Shawna Heilman – Installation

Gene Lohrmeyer – GeoProcessing

Programmer

GeoPredict – Team

INL:

Chris Oertel - Research and Development

Scientist

John Giles - Research and Development

Scientist

Kara Cafferty - Research and

Development Scientist

Boedre Reynolds - Research and

Development Scientist

Business Problem

Measure at specific locations

Large Area to Monitor

Need to predict Cs-137 at

unmeasured areas

Dynamic Contamination

Patterns (wind, activities at

the site)

With thousands of acres, how can the monitoring be optimized

and defensible?

Data Business Needs

Use measured data to predict the Cs-137 values at

unmeasured locations across the site

Defendable to highest scientific level

Defend site baseline

Alleviate public concerns when trying to attract new missions

Compare post event release levels to radiological baseline

In emergencies, this data is the INL radiological baseline

condition

Previous INL Modeling Efforts

Ordinary Kriging

Standard Technique provided via

GeoStatistical Analyst

Not Robust Enough

Incorrectly predicted high Cs-137

(ground-truthed areas)

Results showed spatial gaps in error surfaces

Disjunctive Kriging with Declustering

Available via GeoStatistical Analyst

Declustering Method DID NOT reflect field

sampling conditions

Declustering by cell or polygon method was not accurate

Models underestimated variability of predicted values

INL Modeling Efforts – Why a new Approach?

Requirements for Predictive Modeling at INL:

Declustering by nearest neighbor techniques

Prediction surface with low uncertainties

Spatial dependencies across entire site

Environmental Parameters need to be used in predictions

Ability to defend DOE/INL positions regarding siting of new projects

Monitoring methods at or beyond EPA scientific protocols

More efficient and dynamic soil monitoring

Combine data from multiple years for predictions

State-of-the-Art defense for key environmental media

Provide a monitoring baseline necessary prior to any new large onsite projects

GeoPredict: Computational model created to best predict the

probability of something occurring…

GIS (Spatial Relationships)

GeoStatistics (Stan Miller)

Unique approach

to GeoEngineers

GeoPredict - What is it?

Archaeology, Contaminants, Environmental, Landslides, Weapons Caching, Human Terrain,

Earthquakes

Open and Scalable Solution Scientific Peer Reviewed Reusable Adaptable

ESRI GIS Application interface(Task Assistant Manager) Computational engine

GeoEngineers created a solution framework called GeoPredict

that leverages a number of data inputs displayed in a map output

GeoPredict

Parameters

Elevation

Slope Percent

Aspect

Wind

Geology

Study Area (A)

A1

A2

A3

A4

A5

Bayesian Calcs.

(B)

B1

Known Areas

C1

C2

C3

C4

C5

Exposure

Planning

Risk

Defensibility

Project Cost

Adjustable Constraints

GeoPredict - The Framework

GeoPredict – What Does it Look Like?• Very complicated science rolled into easy to use interface

Known Areas

Existing GIS DataPhysical Environmental

Elements

GeoPredict - Knowing the Parameters

•Bayesian:=P[(E1/A)(E2/A)(E3/A)(E4/A)(E5/A)]*P(A)

P[E1*E2*E3*E4*E5]

•Kriging with Exhaustive Secondary Information

Kriging with External Secondary Information

ESI = bayesian calculations based on environmental parameters

Kriging with ESI –

Includes environmental parameters (the external secondary information as Bayesian) known to be part of the contamination patterns (wind, soil, slope, etc.) combined with measured locations

Better predictions with lower errors (more confidence in the results)

What’s next? Additional modules, scalable, etc.

GeoPredict - Model Output

Optimize monitoring locations

Error surface demonstrating accuracy of method

Spatial Information with high probability

Spatial product regenerated with new input data

Exposure to

Risk/

Prioritize

Work Effort

Red indicates the optimal

locations for monitoring

GeoPredict - Model Output

• Managing Client Risk• Due Diligence

• Defensible Data

• Responsible monitoring

• Project Delivery• Efficient planning

• Minimize surprises

GeoPredict- Summary

Risk Management Air Monitoring

Contaminants

Archaeology

Landslides

Weapons Caching

Human Terrain

Homeland Security

GeoPredict- Other Configurations

Joanne Markert

Associate, Applied Technology

GeoEngineers, Inc.

253.831.3217

Chris Oertel

Research and Development Scientist

Idaho National Laboratory

208.533.7122

John Giles

Research and Development Scientist

Idaho National Laboratory

208.533.7088

Thank You- Questions