1.
AN ML-BASED META-MODELLING INFRASTRUCTURE
FOR ENVIRONMENTAL MODELS
DOCTORAL SCHOOL OF CIVIL, ENVIRONMENTAL AND
MECHANICAL ENGINEERING
XXXI CYCLE
Admission to the third year
S U P E R V I S O R :
P R . P H D R I C C A R D O R I G O N
C O - A D V I S O R :
P H D O L A F D A V I D
P H D S T U D E N T :
F R A N C E S C O S E R A F I N

2.
Meta-modelling infrastructure for environmental models 16/10/17
Innovative software tools enable the advancement of
modeling methods and techniques.
My objective is to expand the modeling platform OMS-
CSIP by enabling modeling efforts in research
environments to become practical modeling solutions
in the field.
Objective

3.
Meta-modelling infrastructure for environmental models
During the
1st year
I was working on:
• Reproducible
Research
16/10/17

4.
Meta-modelling infrastructure for environmental models
During the
1st year
I was working on:
• Reproducible
Research
• BMI-OMS
16/10/17

5.
Meta-modelling infrastructure for environmental models
During the
1st year
I was working on:
• Reproducible
Research
• BMI-OMS
• Single threaded tree
data structure in
OMS3
16/10/17
Picture credits: Bancheri M., 2017,
PhD Thesis, “A flexible approach to
the estimation of water budgets
and its connection to the travel
time theory”

6.
Meta-modelling infrastructure for environmental models
The 2nd year
16/10/17
I am at Colorado State University since January 2017.
The purpose of this visit is to study the design of and improve the OMS3 framework
by working with my Co-Advisor PhD O. David.
Goals:
• Implement an embedded multi-
threaded directed graph data
structure
• Develop a generic meta-modelling
methodology for the OMS-CSIP
platform
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Cloud
Services
Integration
Platform
Business Activity
Monitoring
Object Modeling
System 3
Database
Management
Systems
Web-Based
Access Services
Cloud Computing
Services

7.
Meta-modelling infrastructure for environmental models
OMS3 application: FICUS
16/10/17
FICUS
Framework for Integrating the Complexity of Urban Systems
FINAL GOAL: Build a software framework that allows geo-spatial
temporal analysis with explicit uncertainty
quantification across all computational models

8.
Meta-modelling infrastructure for environmental models
FICUS project
16/10/17
PURPOSE: Design and develop a computational framework to
support federated models of complex urban systems and
enable information support to the Join Intelligence Preparation
of the Operational Environment (JIPOE)
FRAMEWORK
OMS3:
It connects multi-scale computational models of socio-cultural,
infrastructural and environmental systems.
It supplies software tools for making uncertainty
quantification. Any geo-spatial/temporal computational
model must be uncertainty quantified in order to provide
connection between decision making knowledge gaps and
improved data collection.
Credits: PhD Charles Ehlschlaeger

9.
Meta-modelling infrastructure for environmental models
OMS3
My
contribution
I added two new features
to OMS3:
16/10/17
Input
Output
Java
Legend:

10.
Meta-modelling infrastructure for environmental models
OMS3
RUG model
My
contribution
I added two new features
to OMS3:
• R binding
16/10/17
Development
Analysis
Input
Travel Time
Analysis
Output
Attractor
Analysis
R
Java
Legend:

11.
Meta-modelling infrastructure for environmental models
OMS3
RUG model
My
contribution
I added two new features
to OMS3:
• R binding
• Python binding
16/10/17
Development
Analysis
Input
Travel Time
Analysis
Output
Attractor
Analysis
TRANSIMS
Python
R
Java
Legend:

12.
Meta-modelling infrastructure for environmental models
OMS3
RUG model
My
contribution
I added two new features
to OMS3:
• R binding
• Python binding
OMS3 is now available
as a Docker image
16/10/17
Docker
Development
Analysis
Input
Travel Time
Analysis
Output
Attractor
Analysis
TRANSIMS
Python
R
Java
Legend:

13.
Meta-modelling infrastructure for environmental models 16/10/17
The Visualization Component

14.
Meta-modelling infrastructure for environmental models 16/10/17
The Visualization Component
Picture credits: docker.io

15.
Meta-modelling infrastructure for environmental models
The Graph Data
Structure
16/10/17
The single-threaded directed tree data structure is now a multi-threaded acyclic
directed graph data structure fully integrated in OMS3.
Enhanced functionalities:
1. Implicit multithreading computation
2. Improved simulation DSL
3. More flexible calibration set up
4. Increased modeling flexibility

16.
Meta-modelling infrastructure for environmental models
Enhanced
modeling
flexibility
• Different entities
connected to each
other
• Different
computation for each
entity
• Watersheds
separately simulated
and then connected
into a bigger one
• Reservoir model,
each reservoir is a
node
16/10/17

17.
Meta-modelling infrastructure for environmental models
Enhanced
modeling
flexibility
• Modeling of higher
complex systems
including multiple
outputs from a single
node
• Exposure of model
(geo-spatial) state
variables for ANN
training
16/10/17
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16142
1
3
Matching modeling
analysis
Legend:

18.
Meta-modelling infrastructure for environmental models
Application: Posina river (Bancheri M.’s PhD thesis)
16/10/17
Picture credits: Bancheri M., 2017, PhD Thesis, “A flexible approach to the estimation of water budgets and its connection to
the travel time theory”
The Graph Data
Structure

19.
Meta-modelling infrastructure for environmental models
Future developments
16/10/17
● Improve implicit parallelization
● Turn the acyclic graph into a full graph
● Make calibrators working “per branch”
● Make graph callable from within a node (nested
graph)
The Graph Data
Structure

20.
Meta-modelling infrastructure for environmental models
Research environments Planning environments
16/10/17
● Most accurate physically-
based models
● High level complexity to
manage and calibrate models
● Many parameters required
● High Resolution data
● Long computational time
● Deep knowledge and model
understanding
● Simplified modeling
solution
● “Good-enough” accuracy of
results
● Few or no available data
and parameters
● Limited data availability
● quick result feedback
● Not a modeling expert,
limited expertise
ML meta-modelling in
OMS-CSIP

21.
Meta-modelling infrastructure for environmental models
ML meta-modelling in
OMS-CSIP
Research environments Applicative environments
16/10/17
● Always more accurate
physically-based models
● High level complexity to
manage and calibrate
models
● Many data and parameters
required to “feed” them
● Long computational time
● Necessity of not too
accurate results
● Lacking of expertise to
run complex models
● Few or no available data
and parameters
● Necessity of quick
results
How can we bridge the gap between
these two “worlds”?

22.
Meta-modelling infrastructure for environmental models
Meta-modelling in OMS-CSIP: the new layer
16/10/17
Research
Environment
Planning
Environment
OMS-CSIP
Physical
Models
ANNs
(NEAT)
ML meta-modelling in
OMS-CSIP

23.
Meta-modelling infrastructure for environmental models
NEAT: NeuroEvolution of Augmenting Topology
16/10/17
Background
Stanley, Kenneth O., and Risto Miikkulainen. “Evolving neural networks through
augmenting topologies.” Evolutionary computation 10.2 (2002): 99-127
Heaton T. Jeff. 2005. Introduction to Neural Networks with Java. Heaton
Research, Inc..
DEFINITION: It is a genetic algorithm, which can generate the neural network
during the training phase. It works on altering weighting
parameters and the structure of the network at the same time.
LIBRARY: The open source software (https://github.com/sidereus3/ann)
depends on ENCOG v3 for JAVA, open source framework
released under Apache 2.0 license
https://github.com/encog/encog-java-core

24.
Meta-modelling infrastructure for environmental models
Application: East River watershed
16/10/17
NEAT trained on daily data 1994-2007
2011 2012 2013 2014 2015 2016
05001000150020002500
Run 2d forecast − NEAT − precipOnly
Time[d]
Flowrate[in3/d]
measured
modeled
ML meta-modelling in
OMS-CSIP

25.
Meta-modelling infrastructure for environmental models
Application: East River watershed (PRMS comparison)
16/10/17
NEAT trained on daily data 1994-2007
2011 2012 2013 2014 2015 2016
0100020003000400050006000
Run 2d forecast − PRMS − precipOnly
Time[d]
Flowrate[in3/d]
ML meta-modelling in
OMS-CSIP

26.
Meta-modelling infrastructure for environmental models
Application: erosion in Iowa (RUSLE2)
16/10/17
NEAT trained on 400 samples
ML meta-modelling in
OMS-CSIP
0 10 20 30 40
5101520
RMSE: 0.247564309224066
# sample
erosion[tonsperhectare]
expected
observed

27.
Meta-modelling infrastructure for environmental models
Infrastructure: the big picture
16/10/17
UNCERTAINTY QUANTIFICATION
NE
AT
NE
AT
NE
AT
NE
AT
NE
AT
USER
DBs
MeteoData
Physical model
ML meta-modelling in
OMS-CSIP

28.
Meta-modelling infrastructure for environmental models
Scientific Output
Papers:
- Serafin F., Bancheri M., Rigon R. & David O. – A flexible graph data structure into the Object Modeling System:
design and applications – in preparation
- Serafin F., David O., Westervelt J. & Ehlschlaeger C. – Enabling intercommunication between programming
languages into the Object Modeling System: the R and Python bindings – in preparation
- Bancheri M., Serafin F., Bottazzi M., Abera W. , Formetta G. & Rigon R. - A well engineered implementation of Kriging
tools in the Object Modelling System v.3 – in preparation
- Bancheri M., Serafin F., & Rigon R. - Travel time consequences of different schemes of hydrological models – in
preparation
Conferences:
• EGU General Assembly Conference, Vienna (Austria), 23 – 28 April 2017. Presentation: Bancheri, M., Serafin, F.,
Formetta, G., Rigon, R., & David, O. ”JGrass-NewAge hydrological system: an open-source platform for the replicability
of science.”
• EGU General Assembly Conference, Vienna (Austria), 23 – 28 April 2017. Presentation: Tubini, N., Serafin, F., Gruber,
S., Casulli, V., & Rigon, R. ”New insights in permafrost modelling.”
• EGU General Assembly Conference, Vienna (Austria), 23 – 28 April 2017. Short Course: Lombardo, L., Formetta, G.,
Serafin, F., Rigon, R., & Albano, R. “Open-source software for simulating hillslope hydrology and stability.”
• NGA Tech Showcase West, 17 – 18 October 2017. Demo: Serafin, F. & David, O. “FICUS: R binding, Python binding
bundled with OMS3 into a easily runnable Docker image”.
• NGA Tech Showcase West, 17 – 18 October 2017. Demo: David, O., Patterson, D., & Serafin, F. “FICUS: Visualization
component”.
• AGU Fall Meeting, New Orleans (USA), 11 – 15 December 2017. Poster: Serafin, F., Bancheri, M., David, O., & Rigon, R.
“On complex representation and computation of hydrological quantities”.
• AGU Fall Meeting, New Orleans (USA), 11 – 15 December 2017. Presentation: Rigon, R., Bancheri, M., Serafin, F.,
Abera, W., & Bottazzi, M., “Strategies for estimating the water budget at different scales using the Jgrass-NewAGE
system”.
16/10/17

29.
Meta-modelling infrastructure for environmental models
GEOtop: dockerized version
16/10/17
GEOtop was dockerized and
made runnable from within
OMS3 for the short course at
EGU 2017:
Lombardo, L., Formetta, G.,
Serafin, F., Rigon, R., &
Albano, R., “Open-source
software for simulating
hillslope hydrology and
stability”
The main page is available at
https://hub.docker.com/r/
omslab/geotop/

30.
THANK YOU
FOR YOUR
ATTENTION!

31.
Meta-modelling infrastructure for environmental models
Acronyms
16/10/17
• ANN: Artificial Neural Network
• BMI: Basic Modeling Interface
• CSIP: Cloud Services Integration Platform
• DSL: Domain Specific Language
• FICUS: Framework for Integrating the Complexity of Urban Systems
• NEAT: NeuroEvolution of Augmenting Topologies
• OMS: Object Modeling System
• RUG: Regional Urban Growth
• RUSLE: Revised Universal Soil Loss Equation
• TRANSIMS: TRansportation ANalysis SIMulation System