prediksi banjir dki dengan anfis 09
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Prediksi Iklim dan Prediksi Iklim dan Banjir DKI dengan AnfisBanjir DKI dengan Anfis
The Houw LiongThe Houw Liong
P.M.SiregarP.M.Siregar
R.GernowoR.Gernowo
H.WidodoH.Widodo
S. NuryantoS. Nuryanto
Garis BesarGaris Besar
• Model Model Iklim dan CuacaIklim dan Cuaca
• Hard Computing & Soft ComputingHard Computing & Soft Computing
• Prediktibilitas , Chaos/Weak CausalityPrediktibilitas , Chaos/Weak Causality
• Kaidah Fuzzy & ANFISKaidah Fuzzy & ANFIS
• Fuzzy ClusteringFuzzy Clustering
• Deret Waktu Bilangan Bintik Matahari, Deret Waktu Bilangan Bintik Matahari, Curah Hujan & Tinggi Muka Air Curah Hujan & Tinggi Muka Air
• KesimpulanKesimpulan
Model AtmosferModel Atmosfer
Model AtmosferModel Atmosfer
Physical ModelPhysical Model
• Cloud Formation : Arakawa , KuoCloud Formation : Arakawa , Kuo• Interactions : lands, oceans, cryosphere, Interactions : lands, oceans, cryosphere,
biosphere biosphere • Forcing : Solar activity, volcanic eruptions, Forcing : Solar activity, volcanic eruptions,
cosmic rayscosmic rays
•PredictabilityPredictability•Chaos, Attractor, weak causalityChaos, Attractor, weak causality
Predictability of a Climate Predictability of a Climate ModelModel
Forecasting Based on Soft Forecasting Based on Soft Computing & Solar cycleComputing & Solar cycle
• Quasi periodic solar cycleQuasi periodic solar cycle
• Sunspot , flare , cme, galactic cosmic Sunspot , flare , cme, galactic cosmic rays, interplanetary magnetic field rays, interplanetary magnetic field and weatherand weather
• ENSO & IODENSO & IOD
• MJOMJO
Sunspot & cosmic raySunspot & cosmic ray
Galactic Cosmic RaysGalactic Cosmic Rays
Cosmic RaysCosmic Rays
Kaidah Samar Sugeno Kaidah Samar Sugeno (Sugeno Fuzzy Rules) (Sugeno Fuzzy Rules)
• Untuk x adalah AUntuk x adalah Ai i dan y adalah Bdan y adalah Bjj maka z adalah pmaka z adalah piix + qx + qjjy + ry + rijij
• Kaidah Belajar /Kaidah Belajar /Learning Rules :Learning Rules :
• δδv_k = - v_k = - η∂η∂e_tot/∂v_ke_tot/∂v_k
Adaptive Neuro Fuzzy Adaptive Neuro Fuzzy Inference SystemInference System
A1
A2
B2
B1 N
N
layer 1layer 2
layer 3
layer 4
layer 5
x y
1w
2w
1w
2w
x y
ANFISANFIS
• Layer 1 Layer 1 ::
•
• x x andand y y areare input input of of ode -i anode -i andd O1,i O1,i isis membership function ofmembership function of fuzzyfuzzy set set A=(A1,A2A=(A1,A2) and B=() and B=(B1 B1 ,,B2B2 )) with with membership function membership function A A isis : :
• •
• ai,bi, ai,bi, andand ci ci areare parameter parameterss• Layer 2 : Layer 2 : output as the product of input output as the product of input
membership functionsmembership functions : :•
2
1,
1,
( ), 1, 2,
( ), 3, 4,
i
i
i A
i B
O x for i or
O y for i
b2
i
i
A
a
cx1
1)x(
2,1i)y()x(wOii BA1i,2
• Layer 3 Layer 3 inin node -i : node -i :
•
• Layer 4 : Layer 4 : Node -i Node -i is is adaptiadaptive ve node node with with funfunctionction node : node :
2,1i,ww
wwO
21
iii,3
)ryqxp(wfwO iiiiiii,4
ANFISANFIS
• Layer 5 : Layer 5 : final final output :output :
•
5
i ii
i ii i
i
w fO w f
w
sspot
0
50
100
150
200
1948
1952
1956
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
2012
sspot
NASA : Prediction of Solar NASA : Prediction of Solar Cycle 24Cycle 24
Pontianak RegionCorrelation Sunspot vs Precip =0.88
0.00
50.00
100.00
150.00
200.00
1948
1951
1954
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
Years
Sun
spot
/Pre
cip
-50.000.0050.00100.00150.00200.00
ave-sunspot ave-precip
Jaya Pura
0.0050.00
100.00150.00200.00250.00300.00350.00
1948
1951
19541957
19601963
19661969
1972
19751978
19811984
19871990
1993
19961999
2002
Years
mm
/mon
th
0.00
50.00
100.00
150.00
200.00
Avg precip sspot
Jakarta
0.00
50.00
100.00
150.00
200.00
250.0019
4819
5119
5419
5719
60
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
Years
mm
/mon
th
0.00
50.00
100.00
150.00
200.00
Avg Precip Avg-sspot
Fuzzy ClusteringFuzzy Clustering• Fuzzy c-means AlgorithmFuzzy c-means Algorithm
• Fix c (2Fix c (2cc n) and select a value for n) and select a value for parameter m’, initialize the partition matrix parameter m’, initialize the partition matrix UU(0)(0), membership functions and the centers . , membership functions and the centers . Each step in this algorithm will labeled r, Each step in this algorithm will labeled r, where r=0,1,2,..where r=0,1,2,..
• Repeat updating the partition matrix for Repeat updating the partition matrix for rrth th step,U step,U (r)(r) until until
• )()1( rr UU
Fuzzy ClusteringFuzzy Clustering
• set r=r+1 set r=r+1
• Calculate the new c centers :Calculate the new c centers :
n
kmik
n
k kjxmik
ijv
1'
1.'
• Calculate the new membership Calculate the new membership functionsfunctions
1)1'/(2
1 )(
)()1(
m
c
j drjk
drikr
ik
• set r=r+1 set r=r+1
• Calculate the new c centers :Calculate the new c centers :
n
kmik
n
k kjxmik
ijv
1'
1.'
Pengelompokan Samar (Fuzzy Pengelompokan Samar (Fuzzy Clustering)Clustering)
IOD from PAOMA ForecastsIOD from PAOMA Forecasts
Multivariate Enso IndexMultivariate Enso Index
Prediksi Curah Hujan Ciliwung Prediksi Curah Hujan Ciliwung Hilir (DKI Jakarta)Hilir (DKI Jakarta)
Ciliw ung Hilir Prediksi September 2009 - Maret 2010
0
2
4
6
8
Jan-04 Oct-04 Jul-05 Apr-06 Jan-07 Oct-07 Jul-08 Apr-09 Jan-10Jumlah Bulanan Rata-Rata
Cur
Huj
an (
x100
mm
)
Data Prediksi ANFIS
Prediksi Pentad Ciliwung Prediksi Pentad Ciliwung HilirHilir
Ciliw ung Hilir Prediksi Oktober 2009-November 2009
0
0.5
1
1.5
2
2.5
3-Jun-08 3-Oct-08 3-Feb-09 3-Jun-09 3-Oct-09PENTAD
Cur
Huj
(x1
00m
m)
Data Prediksi ANFIS
Prediksi Curah Hujan Ciliwung Prediksi Curah Hujan Ciliwung HuluHulu
Ciliw ung Hulu Prediksi September 2009 - Maret 2010
0
2
4
6
8
Jan-04 Sep-04 May-05 Jan-06 Sep-06 May-07 Jan-08 Sep-08 May-09 Jan-10Jumlah Bulanan Rata-Rata
Cur
Huj
an (
x100
mm
)
Data Prediksi ANFIS
Curah Hujan Pentad Ciliwung Curah Hujan Pentad Ciliwung HuluHulu
Ciliw ung Hulu Prediksi Oktober 2009 - November 2009
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
3-Jun-08 1-Oct-08 29-Jan-09 29-May-09 26-Sep-09PENTAD
Cur
Huj
(x1
00m
m).
Data Prediksii ANFIS
Analisis Hujan Ekstrim DKI Analisis Hujan Ekstrim DKI dengan WRFdengan WRF
Data Curah Hujan 2007 BMKG serupa Data Curah Hujan 2007 BMKG serupa
dengan Hasil WRFdengan Hasil WRF
KesimpulanKesimpulan
• ANFIS & ANFIS & Pengelompokan SamarPengelompokan Samar dapat digabungkan untuk prediksi/ dapat digabungkan untuk prediksi/ prakiraan prakiraan Iklim jangka panjang dan Iklim jangka panjang dan menegah.menegah.
• Prediksi Prediksi cuaca cuaca jangka pendek yang jangka pendek yang akurat memerlukan model atmosfer akurat memerlukan model atmosfer dan data radar & satellit cuaca.dan data radar & satellit cuaca.
Banjir 2002Banjir 2002
Ciliw ung Hilir Prediksi Agustus 2008 - April 2009
0
2
4
6
8
Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09Jumlah Bulanan Rata-Rata
Cur
Huj
an (
x100
mm
)
Data Prediksi ANFIS
Ciliw ung Hilir Prediksi September 2008-December 2008
0
0.5
1
1.5
2
2.5
3
3-Aug-07 3-Nov-07 3-Feb-08 3-May-08 3-Aug-08 3-Nov-08PENTAD
Cur
Huj
(x1
00m
m)
Data Prediksi ANFIS
Ciliw ung Hulu Prediksi May 2008 - April 2009
0
2
4
6
8
Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09Jumlah Bulanan Rata-Rata
Cur
Huj
an (
x100
mm
)
Data Prediksi ANFIS
Ciliw ung Hulu Prediksi May 2008 - Desember 2008
0
0.5
1
1.5
2
2.5
2-Sep-07 1-Dec-07 29-Feb-08 29-May-08 27-Aug-08 25-Nov-08PENTAD
Cur
Huj
(x1
00m
m)
Data Prediksii ANFIS
Banjir 2005Banjir 2005
PROBABILITASPROBABILITASINTENSITAS Curah HujanINTENSITAS Curah Hujan
INTENSITAS C.H. ( 1957 - 1988 ) JAKARTA (745 )
0
10
20
30
40
50
60
0-<=
5
>5-<
=10
>10-
<=15
>15-
<=20
>20-
<=25
>25-
<=30
>30-
<=35
>35-
<=40
>40-
<=45
>45-
<=50
>50-
<=55 >5
5
KELAS
PR
OB
(%)
INTENSITAS C.H. ( 1985 - 2003 ) BOGOR
0
10
20
30
40
50
60
0-<
=5
>5
-<=
10
>1
0-<
=1
5
>1
5-<
=2
0
>2
0-<
=2
5
>2
5-<
=3
0
>3
0-<
=3
5
>3
5-<
=4
0
>4
0-<
=4
5
>4
5-<
=5
0
>5
0-<
=5
5
>5
5
KELAS
PR
OB
(%
)
IODIODIOD
-0.60
-0.40
-0.20
0.00
0.20
0.40
0.60
194819511954
19571960
19631966
196919721975
19781981
198419871990
19931996
19992002
200520082011
20142017
Years
IOD
Solar activities &Solar activities & ClimateClimate
Microphysics Cumulus
Solar and its activities PBL
Earth Surface
surface T,Qv,Wind
Surface FluxSH,LH
IncomingSW,LWSurface
Emisi/albedo
Cloud FractionCloud Effects
Cloud detrainment
Elnino-Lanina Years
0,00
50,00
100,00
150,00
200,00
1948
1950
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Years
Su
nsp
ot
Nu
mb
er
sspotLanina Elnino
Positive-Negative of Indian Dipole Mode Years
0,00
50,00
100,00
150,00
200,00
1948
1950
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Years
Su
ns
po
t N
um
be
r
sspotNegative Positive
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