Francesco Serafin

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

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

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

During the 
1st year
I was working on:
• Reproducible
Research
• BMI-OMS
16/10/17

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”

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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Cloud
Services
Integration
Platform
Business Activity
Monitoring
Object Modeling
System 3
Database
Management
Systems
Web-Based
Access Services
Cloud Computing
Services

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

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

OMS3
My
contribution
I added two new features
to OMS3:
16/10/17
Input
Output
Java
Legend:

OMS3
RUG model
My
contribution
to OMS3:
• R binding
16/10/17
Development
Analysis
Input
Travel Time
Analysis
Output
Attractor
Analysis
R
Java
Legend:

OMS3
RUG model
My
contribution
to OMS3:
• R binding
• Python binding
16/10/17
Development
Analysis
Input
Travel Time
Analysis
Output
Attractor
Analysis
TRANSIMS
Python
R
Java
Legend:

OMS3
RUG model
My
contribution
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:

The Visualization Component

The Visualization Component
Picture credits: docker.io

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

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

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
117 6
54 9
8
10
12 13
15
16142
1
3
Matching modeling
analysis
Legend:

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

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

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

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”?

Meta-modelling in OMS-CSIP: the new layer
16/10/17
Research
Environment
Planning
Environment
OMS-CSIP
Physical
Models
ANNs
(NEAT)
OMS-CSIP

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

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
OMS-CSIP

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]
OMS-CSIP

Application: erosion in Iowa (RUSLE2)
16/10/17
NEAT trained on 400 samples
OMS-CSIP
0 10 20 30 40
5101520
RMSE: 0.247564309224066
# sample
erosion[tonsperhectare]
expected
observed

Infrastructure: the big picture
16/10/17
UNCERTAINTY QUANTIFICATION
NE
AT
NE
AT
NE
AT
NE
AT
NE
AT
USER
DBs
MeteoData
Physical model
OMS-CSIP

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

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/

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

Francesco Serafin

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie Francesco Serafin

Ähnlich wie Francesco Serafin (20)

Mehr von Riccardo Rigon

Mehr von Riccardo Rigon (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Francesco Serafin