AIR Centre | Rio de Janeiro

Environmental Modelling and Artificial Intelligence as tools to support 2030 UN SDGs

achievement: Initiatives, R&D Projects and Proposals

Luiz Landau / Luiz Paulo Assad

Universidade Federal do Rio de Janeiro / COPPE

Laboratório de Métodos Computacionais em Engenharia

Rio de Janeiro – Brasil

March 2020

Topics

1. About us

2. Presentation Guidelines

3. R&D Projects and initiatives examples

4. Environmental Modelling and Data Science – synergy with UN SDGs

5. Future Proposals

6. Final Considerations

Modelagem Numérica Ambiental e Ciência de Dados como suporte aos Objetivos de Desenvolvimento Sustentável da ONU

Who we are …

Geophysical

Modelling

Environmental

Modelling

Remote

Sensing

Scientific

Visualization

Computational

Mechanics

LAMCELaboratory of Computational

Methods in Engineering

Environmental

Modelling Group

Laboratory of Computational methods in Engineering

COPPECivil Engineering Program

35 years of existence

Environmental Modelling Research Group

Laboratory of Computational Methods in Engineering (LAMCE)Environmental Modelling Core Group

• Idealize and Execute R & D Projects in the area of computational environmental modelling;

• Academic formation and professional formation in the area of environmental computational

modelling

Activities

• Oceanographic and Meteorological

Data Analysis

• Atmospheric Modelling

• Ocean Modelling

• Climate Modelling

• Pollutant Dispersion Modelling

• Remote Sensing

Multidisciplinary Team

• Engineers

• Oceanographers

• Meteorologists

• Geologists

• Geographers

LAMCE

Geosciences Institute

Native Firms and Startups

Team

AIR Centre | Rio de Janeiro

Data Science

Computational Systems Group

The Computational Systems Group is focused on the development of machine learning techniques and dataintensive computing to model complex systems providing support to engineering applications.

Research Areas

▪ Machine learning

▪ Data-intensive computing

▪ Complex Nets

▪ Bio-inspired optimization

▪ Risk Analysis

▪ Intelligent decision support

systems

Applications

• Environmental

• Urban Mobility

• Meteorology

• Geology

• Engineering

www.ntt.ufrj.br

Laboratory of Computational Methods in Engineering - Infrastructure

LAMCE - Infrastructure

Immersive Visualization facilities

Inside UFRJ Technological Park

High Performance Computing facilities

Remote Sensing acquisition

Studied Areas

R&D projects in Brazil

Environmental Modelling and Data Science – Demands and Benefits for the Society

5/7/2020 7

Marine coastal management

Renewable EnergyMarine pollution

/Food securityOil Spill accidents

Extreme events

Climate Change

Health Management

Commun Demands Synergies withSDGs

Burned

Agriculture

Public Politics

Environmental ModellingAnd Data Science

Presentation Guidelines

Source: The World in 2050 ( 2019)

Source: Sachs et al., 2019

at the end propose projects and frameworks relating the cited tools and climate change demands towards the SDGs achievement using concepts related to the digital revolution transformation.

Environmental Modelling+

Data Sciences

Identify Synergies with projects and

initiatives

Benefits and Challenges for Sustainable Development

Digital Revolution

TWI2050 Conceptual framework

Ocean

Modelling

Observations

Data

Assimilation

Regional Modelling

Remote Sensing, AUVs (Gliders), ASVs, Profilers and Drifters

IS4DVAR

Period: 2012 - 2021

R&D Initiatives - Project Azul – Ocean Observing System for Santos Basin region

Developement of an Ocean Observation System for Santos Basin with continuous and systematic oceanographic data acquisition and implementation of an

operational ocean model with data assimilation.

Atmospheric

ModellingRegional Modelling

Transformation 3• Decabornization• Automated observations• Real Time

Transformation 6, 5 • Forecasts• Web platform

Transformation 6, 5 and 4• Forecasts• Web platform

Transformation 6• Data Sciences to

improve ocean forecasts

Digital Ocean

TWI2050 (2019)

10

Data freely available for scientific community

Private Companies

Government

Academy

Making available to the community, through the internet, the data obtained by the project to support the generation of basic and applied

knowledge regarding ocean dynamics in the Santos Basin region.

Project Azul

Project Azul – Ocean Observing System for Santos Basin

www.projetoazul.eco.brPublic governance promoting the use anddevelopment of science, technology andinnovation for the creation of instrumentsthat seek to mitigate or minimizeenvironmental impact.

+

MODELOS DEEP LEARNING

Data Science – Meteorology Applications – Nowcasting

Proposed Solution

Improve and Adapt existing techniques known as VIDEO PREDICTION DEEP LEARNING ALGORITHMS

..

.

t

t-1

t-2

t-3

It is expected that the DEEP LEARNING algorithm model “learns” atmospheric dynamics from the acquisition of meteorological information (RADAR)

Different Data Sources

Numerical Models

Contribution to LandslidesEarly warning systems

Nowcasting – Deep Learning

Observed

Forecasted

Transformation 5Sustainable cities and communities

Alert Systems

Identify vulnerable areas

Data Science – Meteorology Applications – Nowcasting

Importance to have access of raw data

R&D Initiatives - Project Costa Norte – Environmental Modelling over the Brazilian Amazon Margin

North BrazilCurrent

Winds

Tides

River Discharge

Amazon Continental Shelf and near offshore region

Environmental ForcingSpace and TimeVariability

Multiscale Environmental processes

Global Scale

Regional Scale

Local Scale

Grid Nesting techniques

Develop an operational Marine hydrodynamic modeling system at regional and local scales of interest, using ocean data assimilation techniques and grid nesting technics

100 % funded by

Partnership: Atmosphere

OceanLand

Project Costa Norte – Environmental Modelling over the Brazilian Amazon Margin

Assmilative Solution(9km)

Remote and In situ observations

Non - Assmilative Solution

Solution evaluated with observations data sets Nesting grid

(3 km)

Nesting grid 2(1 km)

Coastal processes modelling

R&D Initiatives - Project Costa Norte – Environmental Modelling over the Brazilian Amazon Margin

Using Remote Sensing techniques to classify RADAR images in order to obtain ecosystems classification

Remote Sensing Development

Project Costa Norte – Environmental Modelling over the Brazilian Amazon Margin

Tools for understanding the impacts associated to Climate Change …

erosionmigration

erosion

No migration

Mangroove areas• Migration• Ecossystem Losses

Susceptibility of the coastal zone to the adverse effects of Climate Change or the inability to manage these effects

Urban Areas• Financial Losses• Quality of life• Health

Index of Coastal Vulnerability due to Climate Change flood processes

Muito alta

Alta

Média

Baixa

Muito baixa

High VulnerabilityLow Vulnerability

Oceanographic conditions

Economicalconditions

Social conditions

Atmospheric conditions

Level of Resilience

In situ

data acquisition

Real Time data analysis Bathymetric Charts

River flow Modelling

Oil Dispersion

Modelling

TA- Solimões (BR)

Atmospheric Modelling

PIATAM Project: Environmental Modelling, Remote Sensing and Data Science

Environmental Modelling and Data Science Applications in Amazon Continental Region

Operational Environment

Environment Sensivity Index Maps Combined flooded areas with ecosystem classification (river or forest)

Guanabara Bay Environmental Observation Platform – Baia Viva Project

The purpose of this platform is to develop and implement a living lab / test bed environment at the UFRJ Science Park that aims to apply and testing

technologies, develop products and services, invest in academical and scientific capacity building and develop actions in ocean literacy.

Guanabara Bay

Hydrodynamic Modelling

Real Time Data Analysis

Atmospheric Modelling

Observations - Data

Pollutant Dispersion

Modelling

Remote sensing – RADAR Capacity Building Education

Smart Cities

Environmental Management

Mitigation of Climate Changes

Generate products to directlysupport different demands and

activities

Health Management

www.baiaviva.com.br

OD Matrix on macrozones

Data Science Applications in urban regions

• 12.085.108 habitants in 23 cities

• 31-Dec-2013 and 01-Jan-2015

• 2.1 billions of registers from 2.9 millions usuaries

• 1.078 towers

Metropolitan Region of Rio de janeiro

Urban Mobility Pattern

Down Town – South Zone

Down Town – Mountain Region

Weekly

Data Science Applications in urban regions

Socioeconomic Pattern Studies

Data Science Applications in urban regions

TraditionalMethodologies

21 type of data

Socioeconomic monitoring

Digital Revolution

Number of return visits

Applied Methodology

1 type of data

Source: TWI2050

1. Context: Why is important to understand oceans heat transports ?AIR CENTRE | Rio de Janeiro - Atlantic Ocean Circulation Modelling Initiatives

Ocean Heat Transports could be divide in two branches:

• Circulation Generated by winds (Surface circulation)

Improve the oceanographic representation of Atlantic Ocean basin Dynamics taking the contributions of the regional oceanographicdevelopments around the Atlantic basin

Regional High Resolution models+

Local Observations

Atlantic Air Centre Model

use local information (models + observations) not assimilated in global solutions

Downscalling

Upscalling

use global ocean models forecast products as boundary conditions

Regional Models

AIR CENTRE | Rio de Janeiro - Atlantic Ocean Circulation Modelling Initiatives

North Brazil Grid

Southeast Brazil Grid

Development of regional ocean models and use of regional ocean data acquired to generate and improve a

General Circulation solution in the Atlantic Ocean.

Cabo Verde

Grid

Angola Grid

Atlantic Ocean Model

Namibia Grid

Downscalling of

Climate Change scenarios

International Global Scale R&D Initiative - “Atlantic Ocean Margins"

24

Atlantic Ocean MarginsInitiative

Physical, Biological and ChemicalOceanographic Processes

Climate Changes Marine pollution Food Security Renewable Energy Capacity Building

Social Economical Impact of the Oceanographic Development over the South Atlantic Ocean Basin

Aligned with different UN SDG’s

Challenge Multidisciplinary + Multi institutional + International

Transformation 6Support education and

Support to understanding natural processes

Transformation 4Sustainable food and oceans

Transformation 6 Digital revolution

Extract ocean knowledge from acquired data

Transformation 4Sustainable food and oceans

5/7/2020 25

Work Packages Activities

Ocean & Atmos Modelling

Data System

Scientific Visualization /

Communication

Capacity Building /Education

Ocean & Atmos Modelling (different scales)Data AssimilationNesting schemesUpscaling / Downscaling

Data and model results management

Remote and in situ data platforms

Immersive environments3D visualizationNon conventional analysis

Environmental Modeling

Microplastics modellingOil spill modellingBiogeochemical ModeliingSediment Transport modelling

Web platformData Sciences

WorkshopsCoursesTechnical supportOcean Literacy

Main Goals

Develop and improve an Atlantic OceanCirculation Solution

Develop a Data System integratedwith the AirDataNet

Develop new perspectives of analysis and visualization and Transference of informatrion

Develop environmental management solutions to mitigate impacts over the ocean

Integrate and improve the oceanographic knowledge through all the Atlantic Ocean coastal countries

How the oceans are important to me?

AIR CENTRE | Rio de Janeiro - Atlantic Ocean Margins initiative

Main contributions

Transformation 1 education

Leave no one behindprinciple

AIR CENTRE | Rio de Janeiro –International Institutions Partnerships efforts related to Computational Modelling

Project South Atlantic Numerical Computational System (Nov 2019)

Interchange between institutions for training in hydrodynamic modelling and development of computer simulation systems for

the Atlantic

Mobility Joint Call

Partner Institutions

ROMS

MOHID

Santa Catarina

Paraná

AIR Centre Data Net - Brazil

Bahia

Rio de Janeiro

Ceará

Integration and support of different Data Methodology Demands

AIR_DataNET | Brasil

AmazôniaData

Analysis

Numerical modelling

Ocean & Atmos

Data Sciences

(AI)

Diagnostic

Diagnostic Prognostic+

+

Diagnostic

Prognostic

Pattern Identification

https://www.nature.com/articles/d41586-019-00556-5

Global Climate Modelling

Atmospheric Modelling

Ocean Modelling

Pollutant Dispersion Modelling

Environmental Data

Social and Economic Data

Urban Planning Data

Health Data

Dia

gn

ost

icP

rog

no

stic

Environmental Modelling – Human Health Potential Applications

High Performance Computing

Data Science

Operational Environment

Health Forecast

Disease Forecast Index

(Epidemiologic ormental)

GIS Concept

space and timeDistribution of DFI

End Users

Disclosure for the Society

Computational Modelling

Data Acquisition

Hurricanes Floods

Land slidesOil Spill accidents

Health Management Institutions

Vacine campaigns urban drainagenetwork

Meteorological information

Social, economical anddemographical information

Level of Resilienceand Vulnerability

space and timeDistribution of vulnerability

and Resilience

Digital Health

How to communicate ?

Project Costa Norte – Environmental Modelling over the Brazilian Amazon Margin

Tools for understanding the impacts associated to Climate Change …

Source: The World in 2050 ( 2019)

Datasets Numerical modelling

Products or Demands

Data ScienceDigital Revolution

Increase Climate Change

Knowledge

A lot of data…

How to extract more information ?

Increasing computational power

Increasing physical representation

Increasing resolution

Revolução digital para o desenvolvimento sustentável

Cidade e Comunidades Sustentáveis

Educação, Gênero e Desigualdade

Saúde, Bem – estar e demografia

Alimentos, terra, água e oceanos sustentáveis Descarbonização energética e

Indústria Sustentável

Transformations impact in different way each SDG

Identification of Positive and negative Feedbacks

Identify the SDG interventions and the best

monitoring indicators

Contribution Index 3

Environmental Modelling and Data Science Developments – Sinergy with SDGs

EnvironmentalSustainability

IncreasingDigitalizationX

Method to identify and quantifycontributions to each SDG

3 – Intermediate output directlytargets SDG2 – Reinforcing – intermediate outputis necessary1 – Enabling – intermediate outputenables the SDG0 – Neutral – intermediate ouput doesnot significantly interact with the SDG

Fits the scoring of the contribution of each

transformation to your country or region

Environmental Modelling and Data Science Developments – Sinergy with SDGs

source: Sachs et al., 2019

Environmental Modelling

Data Science

+

Main associated SGDs

Sankey Diagram

Circularity and Decoupling

Environmental Modelling and Data Science – support to understand the future of SDGs in 2030 and 2050

Fonte: The World in 2050 (2019)

Climate Modelling

IPCCRCP

Scenarios

Downscalling techniques(atmosphere and oceans)

Intervention

What needs to change to achieve the transformation toward sustainability?

support to understand and quantify how the

transformations change sustainability

RegionalResilience

Regional Social, Environmental and

Economical Vulnerability

Identify Potential Changes on Sustainable pathways

Identify the indicators to be monitored

Digital Revolution

Environmental Modelling

Data Science

Future Proposals – Serra da Bocaina Region - Contextualization

National Park of Serra da Bocaina – region presents social, economics and environmental relationship complexities

150 kmRio de janeiro

Economical Aspects - Tourism

Historical cities

waterfalls

Mountains

Beaches

landslides

floods

Forest fires

Diseases

Environmental Aspects

Brazilian Nuclear Park

Shipping Industry

Violence growth

Oil Industry activities

Ilha Grande bay Terminal

QuilombolasIndios

Region is already well studied and has historical datasets (social, economical, environmental and others)

Artisanal Fishing

Aquaculture

Tourism

Industry

Society

Environment

Government

FutureSustainability ?

Diagnostic

(Phase 1)

• Identification of historical data and existing R&D projects and initiatives

• Application of data science techniques to identify process patterns and non obvious relationships

• Define a specific Social Vulnerability Index for the study region

• Identification support of the most important social, economic, environmental and health targets (or indicators) to be monitored

… but before to achieve the future scenarios we need really to understand the processes and relations now (present)

Prognostic

(Phase 2)

• Implementation and operationalization of stochastic and deterministic predictive models (environmental, social,

economic and health)

• Identification of demands for data collections of different natures

• Building a communication bridge with local communities to transfer information and acquire local knowledge.

2030 and 2050 Scenarios

• Implementation of environmental modelling downscaling techniques

• Construction of 2030 and 2050 social, environmental, economical and health scenarios based on IPCC AR6 scenarios

• Identify the SD pathways toward 2030 and 2050 goals

• Propose interventions

Phase 1 Phase 2 Phase 3

2 year

6 years

2 year 2 year

Human Resources

Financial resources

Future Project Proposals – Bocaina Region

Climate Change Temperature Dengue

Extreme Events Mental Health

Food Security

Economy Violence

Atmosphericmodelling

Oceanographicmodelling

Data Science

Fonte: TWI2050 (2019)

“ There are only 10 years to go to mobilize and leverage digital opportunities to

build sustainable societies. Trend reversal is urgent as the world is at a

crossroads. We have only 10 years to learn how to manage and positively use the

societal impacts of digitalization and artificial intelligence, to merge virtual and

physical spaces and realities, and to avoid further erosion of social cohesion. If we

do not manage to get the two fundamentals right – that is, digitalized green

economies and stable, equitable, open digitalized societies – the world will run

into a serious impasse instead of developing further sustainability

transformations “

Conclusion Remarks

AIR Centre

Rio de Janeiro

Obrigado

Obrigado!

Março de 2020

Luiz Paulo Assad / Luiz Landaulpaulo@lamce.coppe.ufrj.br

landau@lamce.coppe.ufrj.br