Andrea Castelletti Dept. of Electronics, Information, and Bioengineering, Politecnico di Milano, Italy Institute of Environmental Engineering ETH-Zurich, Switzerland

QUEBĖC  CITY,  CA  17.09  -­‐    19.09  2014  

FROM  OPERATIONAL  HYDROLOGICAL  FORECAST  TO  RESERVOIR  MANAGEMENT  

OPTIMIZATION  

On the direct use of hydroclimatic information to better inform water reservoir operation

Contributors

FRANCESCA PIANOSI MATTEO GIULIANI SIMONA DENARO

Towards pervasive sensing of the hydrological cycle

CITIZEN SCIENCE

SMART SENSORS

HUMAN SENSORS

LABS ON CHIP

ENVINODES SMART DUST

VIRTUAL SENSORS

REMOTE SENSING

CYBERINFRASTRUCTURES

CROWNSOURCING

Towards pervasive sensing of the hydrological cycle

ground sensors

“Torrent of information”, Economist, 17th Feb 2010

Towards pervasive sensing of the hydrological cycle

remote sensors

ground sensors

“Torrent of information”, Economist, 17th Feb 2010

Towards pervasive sensing of the hydrological cycle

remote sensors

ground sensors

virtual sensors

“Torrent of information”, Economist, 17th Feb 2010

Towards pervasive sensing of the hydrological cycle

remote sensors

ground sensors

virtual sensors

smart sensors

“Torrent of information”, Economist, 17th Feb 2010

… but decisions are made with little information

… but decisions are made with little information

qt+1

st

FLOODS

qt

st

utHYDROPOWER

Controlled system

Optimal control problem

delay  

t m(·) ut st+1reservoir  reservoir  

qt

Feedback control scheme

minut

[J1 J2]

st+1 = f(t, st, ut, qt)ut = m(t, st)ut 2 Ut(st)qt ⇠ �(·)

It

catchment  

More information = smarter decisions (?)

FLOODS

HY

DRO

POW

ER

More information = smarter decisions (?)

FLOODS

HY

DRO

POW

ER

Pareto front with BASIC Information e.g. - doy (RULE CURVE) or - doy + s(t) (OPERATING POLICY)

More information = smarter decisions (?)

FLOODS

HY

DRO

POW

ER

1.  CAN WE DO BETTER?

2.  HOW MUCH BETTER?

3.  HOW?

Pareto front with BASIC Information e.g. - doy (RULE CURVE) or - doy + s(t) (OPERATING POLICY)

Can we do better?

FLOODS

HY

DRO

POW

ER

Pareto front with PERFECT foresight i.e. - doy, s(t), q(t+1), q(t+2), … q(t+h) (IDEAL POLICY)

Pareto front with BASIC Information e.g. - doy (RULE CURVE) or - doy + s(t) (OPERATING POLICY)

The upper bound performance (perfect foresight)

How much better?

FLOODS

HY

DRO

POW

ER

VALUE of EXOGENOUS INFORMATION (VEI)

Pareto front with BASIC Information e.g. - doy (RULE CURVE) or - doy + s(t) (OPERATING POLICY)

Pareto front with PERFECT foresight i.e. - doy, s(t), q(t+1), q(t+2), … q(t+h) (IDEAL POLICY)

The Value of Exogenous Information

How?

FLOODS

HY

DRO

POW

ER

Pareto front with BASIC Information e.g. - doy (RULE CURVE) or - doy + s(t) (OPERATING POLICY)

Pareto front with PERFECT foresight i.e. - doy, s(t), a(t+1), a(t+2), … a(t+h) (IDEAL POLICY)

Selecting appropriate exo information

Pareto front with EXOGENOUS INFO

VALUE of EXOGENOUS INFORMATION (VEI)

on a more technical ground …

1. How to get the upper bound?

Solve a deterministic optimization problem assuming perfect foresight of the future system inputs (here inflows).

q1, q2, . . . , qh

s.t. future inflow time series

minut

[J1 J2]

HY

DRO

POW

ER

FLOODS

IDEAL front

BASIC front

qt+1

st

FLOODS

qt

st

utHYDROPOWER

1. How to get the upper bound?

Solve a deterministic optimization problem assuming perfect foresight of the future system inputs (here inflows).

q1, q2, . . . , qh

s.t. future inflow time series

minut

[J1 J2]

HY

DRO

POW

ER

FLOODS

ideal release trajectories

ideal storage trajectories

IDEAL front

TARGET

BASIC front

qt+1

st

FLOODS

qt

st

utHYDROPOWER

2. How to estimate the potential improvement (VEI)?

Three metrics to quantify the operational Value of Exogenous Information (VEI)

1.  Minimum distance from the target ideal point

2.  Average distance from the target ideal point

3.  Hypervolume

FLOODS

HY

DRO

POW

ER

IDEAL front

BASIC front

FLOODS

HY

DRO

POW

ER

IDEAL front

TARGET

BASIC front

FLOODS

HY

DRO

POW

ER

IDEAL front

TARGET

BASIC front

1 Min distance 2 Ave distance 3 Hypervolume

3. How to select the best exogenous information?

The ideal release sequence is equivalent to an operating policy whose arguments are t, s(t) and the entire sequence of future inflows

u⇤1, u⇤2, . . . , u

⇤h

ut = m(t, st, )qt, qt+1, qt+2, . . . , qh

'

HY

DRO

POW

ER

FLOODS

ideal release trajectories

ideal storage trajectories

IDEAL front

TARGET

BASIC front

3. How to select the best exogenous information?

The ideal release sequence is equivalent to an operating policy whose arguments are t, s(t) and the entire sequence of future inflows

u⇤1, u⇤2, . . . , u

⇤h

ut = m(t, st, )qt, qt+1, qt+2, . . . , qh

'

ut = m(t, st, )It

IDEA: use the hydrometeorologic information available at time t that better works as a surrogate of the future inflow sequence

HY

DRO

POW

ER

FLOODS

ideal release trajectories

ideal storage trajectories

IDEAL front

TARGET

BASIC front

3. How to select the best exogenous information?

The ideal release sequence is equivalent to an operating policy whose arguments are t, s(t) and the entire sequence of future inflows

u⇤1, u⇤2, . . . , u

⇤h

ut = m(t, st, )qt, qt+1, qt+2, . . . , qh

'

ut = m(t, st, )It

IDEA: use the hydrometeorologic information available at time t that better works as a surrogate of the future inflow sequence

HY

DRO

POW

ER

FLOODS

ideal release trajectories

ideal storage trajectories

IDEAL front

TARGET

BASIC front

streamflow prediction

(model based)

3. How to select the best exogenous information?

The ideal release sequence is equivalent to an operating policy whose arguments are t, s(t) and the entire sequence of future inflows

u⇤1, u⇤2, . . . , u

⇤h

ut = m(t, st, )qt, qt+1, qt+2, . . . , qh

'

ut = m(t, st, )It

IDEA: use the hydrometeorologic information available at time t that better works as a surrogate of the future inflow sequence

streamflow prediction

(model based)

select from available hydroclimatic data

(model free)

HY

DRO

POW

ER

FLOODS

ideal release trajectories

ideal storage trajectories

IDEAL front

TARGET

BASIC front

Automatic feature selection

With multiple, possibly redundant informations, and spatial variability, the number of candidate variables can be very high and an empirical selection is not always effective

Automatic feature selection

With multiple, possibly redundant informations, and spatial variability, the number of candidate variables can be very high and an empirical selection is not always effective

IDEA: AUTOMATIC FEATURE SELECTION algorithm

output candidate inputs

ut = m(t, st, )It

For example:

•  Partial Mutual Information index (Sharma 2000; Bowden et al. 2005)

•  minimum redundancy Maximum Relevance (Heiazi and Cai, 2009)

•  Iterative Input variable Selection (Galelli & Castelletti, 2013)

For a review, see

Greer, B.H., S. Galelli, H.R. Maier, A. Castelletti, G.C. Dandy, M.S. Gibbs, An evaluation framework for input variable selection algorithms for environmental data-driven models, EMS.

http://ivs4em.deib.polimi.it

A Control Theory interpretation

delay  

t m(·) ut st+1reservoir  reservoir  

qt

It

catchment  

Feedback + feedforward control

scheme

inflow  predictor  

qt qt+1ut = m(t, st, , , . . .)

Model-based control

A Control Theory interpretation

delay  

t m(·) ut st+1reservoir  reservoir  

qt

It

catchment  

Feedback + feedforward control

scheme

inflow  predictor  

delay  

t m(·) ut st+1reservoir  reservoir  

qt

It

catchment  

Feedback + model free control scheme

info  selecUon  

It

qt qt+1ut = m(t, st, , , . . .) ut = m(t, st, )It

Model-based control

Model-free control

Summary of the procedure

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

1. COMPUTE the UPPER BOUND

Summary of the procedure

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

Summary of the procedure

automatic feature

selection

selected policy

arguments

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

Summary of the procedure

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO 4. REOPTIMIZE the POLICY

Summary of the procedure

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

ex post VEI estimation

5. re-ESTIMATE the VEI

4. REOPTIMIZE the POLICY

NUMERICAL RESULTS Hoa Binh - Vietnam

Hanoi

HoaBinh

TaBu

LaiChau

TamDuong

NamGiang

MuongTe

VuQuangYenBai

BaoLacHaGiang

BacMe

VIETNAM

CHINA

LAOS

CAMBODIA

THAILAND

Da

Thao Lo

Red-Thai Binh River System - Vietnam

Hoa Binh reservoir - Vietnam

Main characteristics

•  Catchment area 52,000 km2

•  Active capacity 6 x 109 m3

•  8 penstocks 2,360 m3/s (240 MW)

•  12 bottom gates 22,000 m3/s

•  6 spillways 14,000 m3/s

•  15% national energy (7,800 GWh)

source: IWRP2008

Operating objectives •  Hydropower production

•  Flood control (Hanoi)

RESERVOIR

CATCHMENT

POWER PLANT

DIVERSION DAM

COMSUMPTIVE USE THAO

LO

DA

HOABINH

Experiment setting

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

ex post VEI estimation

5. re-ESTIMATE the VEI

4. REOPTIMIZE the POLICY

Experiment setting

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

ex post VEI estimation

5. re-ESTIMATE the VEI

4. REOPTIMIZE the POLICY

Deterministic Dynamic Programming

Experiment setting

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

ex post VEI estimation

5. re-ESTIMATE the VEI

4. REOPTIMIZE the POLICY

Deterministic Dynamic Programming

Tree Based Input variable selection (Galelli & Castelletti, 2013)

Experiment setting

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

ex post VEI estimation

5. re-ESTIMATE the VEI

4. REOPTIMIZE the POLICY

Deterministic Dynamic Programming

Tree Based Input variable selection (Galelli & Castelletti, 2013)

Multi-Objective Evolutionary Direct

Policy Search (Giuliani et al. 2014)

MONSOON

Upper bound (perfect foresight)

floods [cm2]

50 100 150 200 250 300 3503

4

5

6

7

8

9

10 x 109

0

sto

rag

e [

m3 ]

doy

average storage 6

7

8

9

10

11

12

13

7/16/2002 7/26/2002 8/05/2002 8/15/2002 8/25/2002

1st flood alarm level @ Hanoi

leve

l [m

]

behaviour on a flood

0 100 200 300 400 500 600 700 800 9002.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55 x 107

Pareto frontier

hyd

rop

ow

er

[kW

h]

RESERVOIR

CATCHMENT

POWER PLANT

DIVERSION DAM

COMSUMPTIVE USE THAO

LO

DA

HOABINH

Candidate variable selection

Hanoi

HoaBinh

TaBu

LaiChau

TamDuong

NamGiang

MuongTe

VuQuangYenBai

BaoLacHaGiang

BacMe

VIETNAM

CHINA

LAOS

CAMBODIA

THAILAND

Da

Thao Lo

•  precipitations

•  areal precipitation

•  streamflows

• doy and storage

Candidate variable selection

Hanoi

HoaBinh

TaBu

LaiChau

TamDuong

NamGiang

MuongTe

VuQuangYenBai

BaoLacHaGiang

BacMe

VIETNAM

CHINA

LAOS

CAMBODIA

THAILAND

Da

Thao Lo

•  precipitations

•  areal precipitation

•  streamflows

• doy and storage

0

0.1

0.2

0.3

0.4

0.5

0.6

0.70.75

R2

doy s(t) qV(t) qTB(t) ….

50 100 150 200 250 300 3503

4

5

6

7

8

9

10 x 109

06

7

8

9

10

11

12

13

7/16/2002 7/26/2002 8/05/2002 8/15/2002 8/25/2002

0 100 200 300 400 500 600 700 800 9002.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55 x 107

MONSOON

Basic information (doy, i.e. rule curve)

floods [cm2]

50 100 150 200 250 300 3503

4

5

6

7

8

9

10 x 109

0

sto

rag

e [

m3 ]

doy

average storage

Pareto frontier

hyd

rop

ow

er

[kW

h]

1st flood alarm level @ Hanoi

leve

l [m

]

behaviour on a flood

0

0.1

0.2

0.3

0.4

0.5

0.6

0.70.75

R2 policy parameters

doy

MONSOON

0 100 200 300 400 500 600 700 800 9002.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55 x 107

50 100 150 200 250 300 3503

4

5

6

7

8

9

10 x 109

06

7

8

9

10

11

12

13

7/16/2002 7/26/2002 8/05/2002 8/15/2002 8/25/2002

Basic information (doy + s(t))

floods [cm2]

sto

rag

e [

m3 ]

doy

average storage

Pareto frontier

hyd

rop

ow

er

[kW

h]

1st flood alarm level @ Hanoi

leve

l [m

]

behaviour on a flood

0

0.1

0.2

0.3

0.4

0.5

0.6

0.70.75

R2 policy parameters

doy s(t)

MONSOON

0 100 200 300 400 500 600 700 800 9002.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55 x 107

50 100 150 200 250 300 3503

4

5

6

7

8

9

10 x 109

06

7

8

9

10

11

12

13

7/16/2002 7/26/2002 8/05/2002 8/15/2002 8/25/2002

Improved policy (doy + s(t) + qv(t))

floods [cm2]

sto

rag

e [

m3 ]

doy

average storage

Pareto frontier

hyd

rop

ow

er

[kW

h]

1st flood alarm level @ Hanoi

leve

l [m

]

behaviour on a flood

0

0.1

0.2

0.3

0.4

0.5

0.6

0.70.75

R2 policy parameters

doy s(t) qv(t)

MONSOON

0 100 200 300 400 500 600 700 800 9002.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55 x 107

50 100 150 200 250 300 3503

4

5

6

7

8

9

10 x 109

06

7

8

9

10

11

12

13

7/16/2002 7/26/2002 8/05/2002 8/15/2002 8/25/2002

Improved policy (doy + s(t) + qv(t) + qtb(t))

floods [cm2]

sto

rag

e [

m3 ]

doy

average storage

Pareto frontier

hyd

rop

ow

er

[kW

h]

1st flood alarm level @ Hanoi

leve

l [m

]

behaviour on a flood

0

0.1

0.2

0.3

0.4

0.5

0.6

0.70.75

R2 policy parameters

doy s(t) qv(t) qtb(t)

Estimating the Value of Exogenous Information

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

c) hypervolumea) minimum (normalized)distance from targetsolution

b) average (normalized)distance from targetsolution

Our approach vs ISO

automatic feature

selection

stochastic optimization

selected policy

arguments

improved policy

candidate variable selection

hydroclimatic time series

candidate policy arguments

ideal release trajectories deterministic

optimization

future inflow time series

ideal storage trajectories

ex ante VEI estimation

1. COMPUTE the UPPER BOUND

2. ESTIMATE the VEI

3. SELECT the BEST INFO

ex post VEI estimation

5. re-ESTIMATE the VEI

4. REOPTIMIZE the POLICY

0 100 200 300 400 500 600 700 800 9002

2.1

2.2

2.3

2.4

2.5

2.6 x 107

flood

hydr

opow

er

DDPI = (doy)I = (doy,st)

I = (doy,st,Vq)I = (doy,st,Vq,Ta)

EMODPS ISO

targetsolution

Our approach vs ISO

NUMERICAL RESULTS Lake Como - Italy

Lake Como system - Italy

Main characteristics

•  Catchment area 4,509 km2

•  Active capacity 2.47 x 106 m3

•  Average annual supply 4,4 x 109 m3

•  Irrigated area 1,320 km2

•  52% maize (1.5 x 106 ton/year)

Operating objectives •  Water supply

•  Flood control (Como)

River Adda

River Adda

R1

R2

H2

H3

H1

Kilometers0 5 10 20 30 40 50

LegendCatchment area

Irrigated area

Hydropower plant

Reservoir

LakeComo

Lake Como system - Italy

Main characteristics

•  Catchment area 4,509 km2

•  Active capacity 2.47 x 106 m3

•  Average annual supply 4,4 x 109 m3

•  Irrigated area 1,320 km2

•  52% maize (1.5 x 106 ton/year)

Operating objectives •  Water supply

•  Flood control (Como)

J F M A M J J A S O N D50

100

150

200

250

Dem

and

[m3 /

s]

(a)

J F M A M J J A S O N D0

500

1000

1500

2000

2500

Pric

e [e

uro/

MW

]

(b)

J F M A M J J A S O N D−20’000

−10’000

0

10’000

20’000

30’000

Reve

nue [euro

/day]

(c)

Time [days]

FIG. 5. (a): Yearly pattern of water demand. (b): Yearly pattern of the energy price(each colour band represents the energy price in the j-th most profitable hour). (c):Di↵erence in daily hydropower revenue (14-days moving average over years 1996-2005)between centralized policy C6 and uncoordinated UC.

25

water demand

J F M A M J J A S O N D0

10

20

30

40

Flow

[m3 /s]

Inflow Release

J F M A M J J A S O N D50

100

150

200

250

Flow

[m3 /s]

Time [days]

(a)

(b)

FIG. 2. Historical inflow (dashed) and release (solid) of the hydropower reservoir R1(a) and lake Como (b) (14-days moving median over the period 1996-2005).

22

inflow/outflow

River Adda

River Adda

R1

R2

H2

H3

H1

Kilometers0 5 10 20 30 40 50

LegendCatchment area

Irrigated area

Hydropower plant

Reservoir

LakeComo

Upper bound (perfect foresight)

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

Pareto frontier

qt+1

st

FLOODS

qt

st

ut

IRRIGATION

Candidate variable selection

•  doy

•  lake level h(t)

•  areal solid precipitation (t-1)

•  areal rainfall (t-1)

•  0 °C isotherm (t-1)

Candidate variable selection

Hydropower reservoirs:

•  Inflow q(t-1)

•  Storage s(t)

•  Release r (t-1)

Candidate variable selection

areal SWE and melting inferred from SWE stations

•  total SWE(t)

•  free SWE(t)

•  total melting(t)

•  free melting(t)

Candidate variable selection

•  doy and h(t) Como

•  precipitation rain and 0 °C isotherm

•  HP reservoirs: q(t-1), s(t) and r(t-1)

•  SWE(t) and melting(t)

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

R2

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

R2

policy parameters Pareto frontier

Basic information (doy)

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

policy parameters

R2

Pareto frontier

Basic information (doy+h(t))

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

policy parameters

R2

Pareto frontier

Basic information (doy+h(t)+q(t-1))

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

policy parameters

R2

Pareto frontier

Basic information (doy+h(t)+q(t-1)+free SWE(t))

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

policy parameters

R2

Pareto frontier

Basic information (doy+h(t)+q(t-1)+free SWE(t)+prec(t-1))

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

policy parameters

R2

Pareto frontier

Basic information (doy+h(t)+q(t-1)+free SWE(t)+prec(t-1)+s(t))

Yearly days of flood [#]

Irrig

atio

n d

efic

it2 [

(m2 /

s)2 ]

Conclusions

§  Enlarging the information system used to make decision might be a way to

improve operation efficiency and ultimately reduce vulnerability

§  Tools and procedure to quantitatively assess the space for improvement

and identify the most informative variables are very useful.

§  Skipping the use of models and directly using raw information seems to be

an interesting option when streamflow prediction are not available ….

§  … or not reliable, e.g. mid-long term

Future VIETNAM Preliminary results using ENSO

0 100 200 300 400 500 600 700 800 9002.05

2.1

2.15

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55 x 107

flood

hydr

opow

er

Future Lake Como – SNOW WATCH

Where to find the code

Input Variable Selection ivs4em.deib.polimi.it

Where to find the code

BORG MOEA borgmoea.org

THANKS