Indian Journal of Geo Marine Sciences
Vol. 46 (04), April 2017, pp. 669-677
Can the Drought/Flood Monsoon Conditions over the Indian
subcontinent be forecasted using Artificial Neural Networks?
Maya L. Pai1, K. V. Pramod2, A. N. Balchand3* & M. R. Ramesh Kumar4
1
Department of Computer Science and IT, School of Arts and Sciences, Amrita University, Cochin 682024
2
Department of Computer Applications, Cochin University of Science and Technology, Cochin 682022
3*
Department of Physical Oceanography, Cochin University of Science and Technology, Cochin 682016
4
Physical Oceanography Division, NIO Goa 403004
*[Email: [email protected]]
Received 26 December 2014 ; revised 26 February 2015
The Indian summer monsoon rainfall during the months June, July, August and September (JJAS) has been classified into seven
climatic zones, according to standard precipitation index. Prediction of rainfall within the six hydrological zones of India was
attempted with the oceanic predictors, which highly influence the terrestrial precipitation, such as Sea Surface Temperature
(SST), Sea Level Pressure (SLP), Humidity and zonal and meridional components of Surface Wind (u and v) to quantify the
rainfall amounts by clustering based artificial neural networks for the distinguishable dry and wet years. In the present analysis,
we have used data for the period 1960 – 2012, which incidentally had several extreme events (of drought and flood conditions)
over the Indian subcontinent. Next, the results indicate that the predicted values are well comparable with the actual measured
values proving the usefulness of this approach. In addition, this approach has improved upon the past and recent attempts to
model rainfall (including extreme cases) which in turn will have a significant impact on farmers and agriculturists.
[Keywords : Hydrological Zones, Monsoon Rainfall, Clustering, Artificial Neural Networks, Self-organizing Map, Standard
Precipitation Index]
Introduction
The Indian monsoon is of historical importance
to both people of the Indian subcontinent and to
the ecosystem of Indian Ocean1. Krishna Kumar
et al. have made a detailed review on seasonal
forecasting of Indian summer monsoon rainfall
(ISMR)2. Many attempts were thenceforth
carried out towards improved understanding of
this phenomenon in totality with the aid of
advanced regression and parametric models
aimed at long range prediction. Once the
limitations of these statistical methods were
recognized, attempts were made to develop
better models using tools like - Artificial Neural
Networks (ANN), which have the capability to
capture the patterns hidden in data sets and
therefore are applied for classification and
prediction. Guhathakurta et al. developed a
hybrid principal component model using 8
parameters3. They used 30 years data (1958-87)
for training and 10 years (1988-97) for
validation and the resulting RMSE was found to
be 4.93%. Nagesh Kumar et al. developed an
artificial intelligent model for rainfall
forecasting of Orissa state on monthly and
seasonal bases4. The empirical method based on
time series analysis on the other hand uses only
the past rainfall data and does not use any
predictors5. Venkateswan et al. have predicted
Indian monsoon rainfall with the help of certain
predictors and compared the results with linear
regression techniques6. Sahai et al. applied
ANN techniques to the monthly time series
rainfall data of June, July, August and
September and observed that ANN gave better
results than regression models7. Iyengar and
Raghunath too used ANN for predicting ISMR
wherein they first divided the whole time series
into linear and nonlinear parts and then applied
ANN to the nonlinear part8. These attempts
were mostly carried out for the whole of India
and have led to limited predictive results.
During 1965 and 1966, major parts of India
were under prolonged and severe drought
conditions due to deficient monsoon rainfall.
The Drought Research Unit (DRU) of India
started conducting studies on different aspects
670
INDIAN J. MAR. SCI., VOL. 46, NO. 04, APRIL 2017
of the Drought Standardized Precipitation Index
(SPI) developed by Mckee et al.9. The SPI is a
tool developed for monitoring drought and is
based on the precipitation data. According to
the DRU and the Indian Meteorological
Department (IMD), the years 1965, 1966, 1972,
1974, 1979, 1982, 1986, 1987, 2002 and 2004
were identified as dry/drought/deficit years and
1961, 1970, 1975, 1983, 1988 and 1994 as
wet/flood/excess years10. Maya et al. have
developed a model for long range forecast
within 10x10 grid of Indian sub-continent for the
south-west ISMR using the ANN technique11.
The present work is a further extension of this
model to predict rainfall in the six hydrological
climate zones of India and re-identify the
drought/flood years already categorized
according to the SPI classification, for its
successful prediction.
The empirical forecasting of Indian monsoon
rainfall had been performed using combinations
of climatic parameters, such as the atmospheric
pressure, wind, sea surface temperature (SST),
snow cover and the phase of El Niño–Southern
Oscillation (ENSO)5. The regression models
have been able to predict 60–80% of the total
seasonal Indian rainfall by the month of May
preceding the summer monsoon7. In these
studies, the SST is an important oceanic
parameter as it directly influences the air-sea
exchange of heat. Additionally, we have also
considered the parameters, sea level pressure
(SLP), humidity, zonal (u) and meridional (v)
winds for the prediction of rainfall in six
divisions within India.
Hydrological Climatic Zones of India
On the basis of rainfall distribution and other
meteorological parameters, India has been
divided into different homogeneous subdivisions. Using rainfall data of stations for the
period 1901-1950, the IMD has published a
comprehensive Rainfall Atlas of India in 1981,
which contains 98 maps on different aspects of
rainfall distribution. According to Koppen
classification, India is divided into six
hydrological climatic zones, namely Desert,
Semi Arid, Hill type, Humid Subtropical,
Tropical wet and dry and Tropical wet12 (see
Fig. 1).
The rainfall data for Indian
subcontinent fall within a total of 347 grids of 10
x 10. The Desert region (55 grids) consists
mainly of Rajasthan and falls within 23.50 to
31.50 N and 69.50 to 75.50E. Parts of Gujarat and
Karnataka constitute the Semi Arid region of 94
grids (8.50 to 32.50 N; 70.50 to 79.50 E). The
Hill type consists of 51 grids covering Himachal
Pradesh and Arunachal Pradesh (26.50 to 36.50
N; 80.50 to 97.50E). The Humid Sub tropical
consists of Uttar Pradesh having 108 grids
(21.50 to 31.50 N; 77.50 to 97.50E). The Tropical
wet and dry includes the states of Tamil Nadu
and Andhra Pradesh having 104 grids (11.50 to
23.50 N; 76.50 to 88.50 E). Kerala and Goa
constitute 67 grids of Tropical Wet (8.50 to
21.50 N & 69.50 to 78.50E).
Fig. 1: Hydrological regimes of India [Indian Climatic
Zone Map, after Koppen (Heitzman and Worden, 1996)12.]
Materials and methods
Data
The inputs needed for the present study such as
SST, SLP, humidity and the zonal (U) and
meridional (V) winds for the Indian ocean (IO)
region (300S- 300N and 400E- 1200E) were
extracted from the International Comprehensive
Ocean-Atmosphere Data Set (ICOADS) sourced
www.esrl.noaa.gov/psd/
from
site
data/gridded/data.coads.1deg.html in 10 x 10
grid (60 x 80 grids)13. They have prepared it for
10x10 boxes since 1960, after the climatological
outlier trimming14. The variables are
summarized with a set of 10 statistics, namely
mean, median and the number of observations15.
These attributes during the pre-monsoon
months, viz. March, April and May (MAM) act
as inputs for the neural network. The daily 10x10
gridded rainfall data16 were collected from the
IMD site (http://www.imdpune.gov.in) for the
period 1960-2004 for the monsoon months, June
to September (JJAS); and were subsequently
updated to the year, 201217.
671
PAI et al.: FORECAST OF DROUGHT/FLOOD MONSOON CONDITIONS-INDIAN SUBCONTINENT
Clustering Approach
We employ clustering to improve the
understanding of climatic processes, since it is
difficult to analyze and understand large data
sets; in order to assess the quality of climate
model results and to identify prevailing system
features and their scales for atmospheric
regimes18, this approach was found effective.
The goal here is to discover the underlying
structure within the data. Kohonon's Self
Organizing Maps (SOM) is a clustering and data
visualization technique based on neural
networks in which the networks learn to form
their own classifications of the training data
without the help of a supervisor and is referred
to as unsupervised learning. Based on ANN,
the most common neural network model is the
Multi Layer Perceptron (MLP).
Cluster Validation
There are several cluster validity measurement
techniques proposed by different authors19. The
criteria widely accepted among them for
partitioning a data set into a number of clusters
are: a) the separation of clusters and b) the
compactness. Halkidi et al. define the clustering
validity index, S_Dbw based on the cluster’s
compactness (in terms of intra-cluster variance)
and the density between clusters (in terms of
inter-cluster density)20.
Inter-Cluster Density (ID) - It evaluates the
average density in the region among clusters in
relation with the density of clusters.
Dens _ bw  c  
density(uij )
1 c c

c(c  1) i 1 j 1 max{density(vi )density(v j )}
(1)
where vi and v j are centers of clusters, ci and
c j respectively and uij the middle point of the
line segment defined by the cluster’s centers vi
Also
f  x, u   0, if d  x, u   stdev
1, otherwise
stdev 
where
Density  u    f  xi , u 
i 1
(2)
where nij is number of tuples that belong to the
clusters, ci and c j , i.e. xi  ci  c j  S
represents the number of points in the
neighborhood of u .
c
  v 
i
i 1
is the average
standard deviation of clusters.
Intra-cluster variance – The average scattering
for clusters
Scat  c  
The p
 xp 
th
1 c   vi 

c i 1   S 
dimension of   S  is defined by
2
1 n
xkp  x p  and that of   vi  is


n k 1
p
given by  v 
i
validity
1
ni
ni
 x
k 1
index
p
k
 vi p  . Then the
is
2
defined
S_Dbw  c   Scat  c   Dens _ bw  c 
as
(4)
The cluster number that minimizes the above
index has been considered as the optimal value
and is used in the present study.
Silhoutte coefficient – For a data set D , of n
objects, let D be partitioned into k clusters
c1 ,..., ck . For each object o  D , a  o  is the
average distance between o and all other
objects in the cluster to which o belongs. b  o 
is the minimum average distance from o to all
clusters to which o does not belong21. For
o  ci ,1  i  k , a(o) 
and v j
nij
1
c
and b(o) 

oci ,o  o
dist (o, o)
ci  1
  oc j ,o o dist (o, o) 
min 
 .
c j :1 j  k , j i
c
j


The Silhoutte coefficient is then given by
s ( o) 
(5)
b (o )  a ( o)
max  a(o), b(o)
672
INDIAN J. MAR. SCI., VOL. 46, NO. 04, APRIL 2017
Standard Precipitation Index (SPI)
The meteorological droughts are classified into
(a) moderate and (b) severe based on rainfall
deficiency, i.e. 26 to 50% and more than 50%
respectively. As SPI values fit a typical normal
distribution (see Fig. 2), we expect these values
to be within one standard deviation
approximately 68% of time, within two standard
deviations, 95% of time and within three
standard deviations, 99% of the time22.. Table1
shows the classification of rainfall using rainfall
values.
Methodology
To preserve consistency, the inputs are
preprocessed and the missing values are filled
via spline interpolation. The parameters, viz.
SST, SLP, humidity, u wind and v wind of the
whole Indian Ocean act as inputs for the
network. The target data, rainfall is clustered
using an unsupervised visualization technique
called SOM. By attempting so, each object in
the data set is assigned to the centroid which is
the best approximation of that object23, 24. The
whole data is divided into 6 hydrological
climatic zones of 10x10 grids. The 5 inputs are
fed to the network to predict rainfall in each grid
for all 6 regions. Forty years (1960-1999) of
data are used for training, eight years for
validation (2000-2007) and five years (20082012) for testing. The numerical simulations
were carried out for different cluster numbers
ranging from 2 to 20 via two different cluster
validation techniques and the error was found to
be minimum for 10 clusters [shown in bold in
Table 2]. The training of the network was
carried out using 40 hidden neurons and each
neuron in the network used a Levenberg Marquardt activation function. The respective
nets were generated using the feed-forward back
propagation algorithm and the net converged
after 500 iterations. The correlation between the
observed and predicted clusters used in training
was found to be 0.785 at 99 % level of
confidence. Once the network was trained, it
was used to predict the rainfall for the years,
2008-2012. The actual and predicted clusters for
this period have a correlation coefficient of
0.427 at 99% level of confidence. The predicted
output is further classified according to Table
125.
Fig. 2: The Normal distribution curve drawn in MATLAB.
INDEX
>2.0
1.5 t0 1.99
1 to 1.49
-0.99 to 0.99
-1.0 to – 1.49
-1.5 to -1.99
< -2
INTERPRETATION
Extremely wet
Very wet
Moderately wet
Near Normal
Moderately dry
Severely dry
Extremely dry
Table1: Standard Precipitation Index classification.
Performance Analysis
The performance of the proposed model is
evaluated using the following parameters:
a) Correlation Coefficient
CC 
n Oi Pi    Oi   Pi 
n   Oi2     Oi  n   Pi 2     Pi 
2
Oi , Pi are the observed and the
where
predicted values of rainfall for the year i
respectively and n is the total number of
years to be predicted.
b)Root Mean Square Error
RMSE 
  y  yˆ 
i
2
i
N
where yi are the observed values, yˆ i are the
predicted values for rainfall and N is the
number of observations.
c) The normalized root-mean-square error
(NRMSE) is the RMSE divided by the range of
2
PAI et al.: FORECAST OF DROUGHT/FLOOD MONSOON CONDITIONS-INDIAN SUBCONTINENT
observed values of a variable being predicted
and is expressed as a percentage.
NRMSE 
RMSE
xmax  xmin
are the maximum
observed values.
where xmax and xmin
and
minimum of the
Results and Discussion
We have been able to predict rainfall of 10x10
grids within six hydrological climatic zones of
India using a set of pre-defined parameters from
the Indian Ocean, by employing clustering. In
order to verify cluster validity, two approaches
were attempted, namely the S_Dbw and the
Silhoutte coefficient; 10 was chosen to be the
optimized cluster number11 and further
processing of rainfall data yielded significant
results pertaining to drought and flood years,
which are considered as extreme events in
hydrology.
Applying this model, we could also very well
predict the outcome during these years. Table 3
shows the error percentage in the prediction of
rainfall for the drought and flood years obtained
from the proposed model. We find that the error
percentage for 8 out of 10 years is within 10%
which is a commendable result. However, in the
case of flood years, the model holds
considerably better results with errors less than
12% [5 out of 7]. Further, the offshoot of 27%
in 2002 is a result of an isolated severe drought
year, beyond the expected range of occurrence.
As with regard to flood years, the off-set is
limited to 15% or less among the two instances
in 1961 and 1994.
Table 4 shows the correlation between the
observed and predicted clusters for the drought
years at 0.01 level of significance. The values
indicate good correlation in most instances [>
0.70], except for the years 2002 and 2004. This
shows that the cluster approach is an acceptable
tool for forecasting. Table 5 gives the range of
rainfall values included in each cluster and the
respective centroid. This table has relevance to
Figs. 3, 6, 4 and 5 which show the observed and
predicted clusters of the ISMR for the years
1960 and 2007- both normal years, 1970 - a
673
flood year and 2002 - a drought year,
respectively. From the figures, it is observed
that for the normal years, 1960 and 2007 the
observed and predicted match extremely well
for 4 hydrological zones namely, Tropical Wet,
Desert, Hill Type and Humid subtropical; to a
certain extent cluster based prediction for the
year 2007 indicates prevalence of dry conditions
extending towards Central India against lesser
prediction values but vice versa for the Southern
Peninsular regions. The percentage error and the
correlation coefficient for the year 1960 are
19% and 0.843 respectively, while for the year
2007 are 32% and 0.545 respectively. The year
1970 which was categorized under flood year
exhibits highly acceptable results when
comparing predicted values to those of observed
on clusters except for a few (say, 4 - 5) grids of
Central Eastern India. Most of the locations
indicate compatibility. With intent, the extreme
drought year of 2002 has been studied for
predictive purpose and the same is compared
with the observed (see Fig. 5). Whereas the
Desert and Humid Sub Tropical regions do
indicate promise, parts of Southern India and
Hill Type regions in the northernmost sector fail
to create matching responses. Nevertheless, the
approach does not fail to satisfy events in the
rest of the 3 hydrological zones. Figure 7 shows
the observed and predicted clusters for the year
2012, which is one of the years used for testing
the model; the error percentage and the
correlation coefficient are 19% and 0.626
respectively. This analysis again is a clear
indicator of the predictive exercises that an
ANN can support. Also, 4 hydrological zones
indicate proven comparison with good cluster
matching. We propose that the said
mathematical tool apart from analyzing normal
or near normal events, could also be applied to
predict extreme events and in cases of drought,
better applicability is achieved compared to
reasonable accuracies in the case of flood
events. The statistics of the results obtained for
the training and the test data are depicted in
Table 6 to further justify the approach adopted
in this study. As a precursor to the rainfall
instances, the oceanic state analyzed through
measurable known variables, will be of
immense value to an agro based nation like
India.
674
INDIAN J. MAR. SCI., VOL. 46, NO. 04, APRIL 2017
Table 2: Optimal partitioning found by S_Dbw index and
Silhoutte coefficient
Number of
clusters
S_Dbw
Silhoutte
coefficient
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1.8319
1.7252
1.6237
1.4794
1.49
1.5795
1.4264
1.3997
1.3851
1.7801
2.4703
NAN
2.1425
2.0444
1.8905
1.7894
1.7315
1.6489
1.5692
-0.4999
0.2309
0.4872
0.7337
0.8016
0.8453
0.8915
0.9245
0.9444
0.9463
0.9121
0.9126
0.9299
0.9571
0.9384
0.9067
0.9764
0.9587
0.9837
Table 4: Correlation coefficient between the observed and
predicted clusters at 99 % level of confidence.
Drought Correlation Flood Correlation
Years
coefficient
1965
0.872
1966
0.852
1972
0.815
1974
0.861
1979
0.843
1982
0.721
1986
0.893
1987
0.763
2002
0.686
2004
0.524
Years coefficient
1961
0.771
1970
0.857
1971
0.792
1975
0.745
1983
0.816
1988
0.772
1994
0.861
Table 3: Percentage errors in drought and flood years
Drought Error % Observed Predicted Flood Error % Observed Predicted
Years
seasonal Seasonal Years
sesaonal seasonal
rainfall (mm) rainfall( mm)
rainfall (mm) rainfall (mm)
1965
1966
1972
1974
1979
1982
1986
1987
2002
2004
5.60%
6.20%
9.10%
5.60%
9.10%
9.80%
6.60%
10%
27%
16.90%
12542.8
13852.43
10937.51
14439.39
12258.17
12781.56
13444.91
12583.22
11535.85
13299.29
13738.48
14240.03
13333.99
14298.04
13520.29
14214.79
14590.73
13485.87
15406.84
15582.12
1961 15%
1970 8%
1971 10%
1975 11.80%
1983 8.50%
1988 11%
1994 13.80%
17012.04
16515.03
15545.61
16592.09
16274.9
16816.3
15517
Fig. 3: Observed and predicted clusters representing
rainfall for the year 1960.
15814.83
16046.44
16315.69
17013.6
15035.23
15552.18
15485.11
Fig. 4: Observed and predicted clusters representing
rainfall for the year 1970.
675
PAI et al.: FORECAST OF DROUGHT/FLOOD MONSOON CONDITIONS-INDIAN SUBCONTINENT
Table 6: Statistics of the results obtained for the training
and the test data
STATISTICS
Seasonal rainfall (JJAS)
Fig. 5: Observed and predicted clusters representing
rainfall for the year 2002.
Fig. 6: Observed and predicted clusters representing
rainfall for the year 2007.
DESERT HILL SEMI HUMID SUB TROPICAL TROPICAL
TYPE ARID TROPICAL WET WET & DRY
TRAINING DATA
Mean observed (mm)
Mean predicted (mm)
SD observed (mm)
SD predicted (mm)
CC between the actual and predicted rainfall
CC between the actual and predicted clusters
RMSE (mm)
NRMSE
12.84
13.63
8.36
9.61
0.56
0.59
8.57
0.14
44.05
46.22
32.18
33.18
0.75
0.77
23.17
0.12
21.05
22.65
11.65
15.42
0.56
0.61
13.2
0.09
40.86
41.08
24.01
25.75
0.69
0.69
19.6
0.1
33.56
33.92
31.26
32.02
0.88
0.88
15.47
0.09
24.8
25.3
14.4
16.64
0.62
0.67
12.3
0.11
TEST DATA
Mean observed (mm)
Mean predicted (mm)
SD observed (mm)
SD predicted (mm)
CC between the actual and predicted rainfall
CC between the actual and predicted clusters
RMSE (mm)
NRMSE
14.68
13.45
8.44
12.16
0.28
0.25
11.27
0.25
38.42
44.88
33.06
35.69
0.42
0.46
36.3
0.23
22.1
23.09
12.73
20.92
0.26
0.24
21.45
0.23
38.06
41.77
19.98
31.32
0.42
0.33
29.39
0.19
35.7
34.03
34.34
32.77
0.74
0.74
24.08
0.15
27.27
28.43
12.32
21.73
0.33
0.36
21.12
0.36
Table 5: Range of values of the clusters and the centroids
covering the full period 1960- 2012.
Clusters Range of values Centroid
in which the
clusters lie
C1
[ 116.55, 188.90] 129.74
C2
[89.80, 114.69 ]
101.06
C3
[69.90, 88.85]
78.4
C4
[53.52, 69.68]
60.9
C5
[41.76, 53.89]
46.8
C6
[32.59, 41.74]
36.7
C7
[24.62, 32.59]
28.47
C8
[16.97, 24.62]
20.7
C9
[9.30, 16.97]
13.17
C10
[0.02, 9.30]
5.4
Fig.7: Observed and predicted rainfall in clusters for the
year 2012.
676
INDIAN J. MAR. SCI., VOL. 46, NO. 04, APRIL 2017
Table 7: Comparison of experimental results of the
proposed model and the model for training and test data
after Singh and Bhogeswar (2013)26
Seasonal rainfall
(mm)
Proposed model
Train
ing
data
(1960
2007)
Test
data
(2008
2012)
Mean
obser
ved
1074.
42
1017.
54
Mean
predic
ted
1113.
72
1106.
82
Singh and
Bhogeswar ( 2013)
model
Mean Mean
obser predic
ved
ted
Train 855.3 852.4
ing 0
0
data
(1876
1960)
Test
data
(1961
2010)
833.0
3
825.9
5
Conclusion
The Indian ocean state variables have a high
influence on the Indian monsoon rainfall which
can be beneficially used for studies in prediction
[of rainfall] within the different hydrological
climatic zones using artificial neural networks;
the 7 classifications, based on the SPI obtained
from IMD were more helpful in this respect.
The model predicts well for almost all grids of
the hydrological regimes of India [refer to table
3, providing direct results on the actual and the
predicted. The drought years have a better
correlation coefficient of more than 0.70 at 99%
level of confidence. The minimum rainfall year,
1972 and the maximum rainfall year,
1961during the period 1960-2012 have
correlation coefficient 0.815 and 0.771 and
could be predicted well with an error of only 9%
and 15% respectively (Table3). Past and recent
studies in this context indicate an overall
improvement in prediction in terms of
application of newer tools as well as in data
mining, apart from computing rainfall values
closer to actuals: the works by Guhathakurta,
Sahai et al., Iyengar et al., and Pritpal et al.,
have made significant contributions3,7,8,26. For
example, tables 6 and 7 indicate the statistics of
the results obtained for the training and the test
data pertaining to this study as well as from
recent and past publications respectively and it
is very obvious that the outcome from this study
utilizing ocean based predictors for each of the
hydrological zones will serve the intended
purpose to a better extent. This study focused on
extreme events, are thus more relevant in the
context of rainfall analyzes to mitigate
hazardous conditions which may prevail on
either case of drought or flood, within a
homogenous division. We wish to point out that
the RMSE, one among many indicators is
helpful to reach a decisive conclusion.
Therefore the model can be used to forecast
Indian monsoon using the pre-monsoon Indian
Ocean parameters, even for cases of drought and
flood. To conclude, the model also shows good
results for almost all grids, especially for the
drought years and for the case tested in normal
years too.
Acknowledgements
The authors thank India Meteorological
Department (IMD) and ICOADS for data and
CUSAT for facilities. Maya thanks Amrita
School of Arts and Sciences for permission to
carry out the work. MRRK would like to thank
the Director, CSIR-NIO, Goa for the facilities
provided for carrying out the work. The authors
would like to acknowledge the use of Freeware
GMT.
References
1.
2.
3.
4.
5.
6.
7.
1.Walker, G.T., Correlation in seasonal
variations of weather II, Mem. India Met. Dep.
XXI.XXII (1910).
Krishna Kumar, K., Soman, M. K. and Rupa
Kumar, K., Seasonal forecasting of Indian
summer monsoon rainfall: a review, Weather,
50(1995) 449-467.
Guhathakurta, P., Rajeevan, M. and Thapliyal,
V., Long range forecasting Indian summer
monsoon rainfall by a hybrid principal
component neural network model, Meteorol.
Atmos. Phys., 71(1999) 255-266.
Nagesh Kumar, D., Janga Reddy, M. and Maity,
R., Regional rainfall forecasting using large scale
climate teleconnections and artificial intelligence
techniques, Journal of Intelligent Systems,
16(2007) 307-322.
Goswami, P. and Srividya, A novel neural
network design for long range prediction of
rainfall pattern, Current Science, 70(1996) 447457.
Venkatesan, C., Raskar, S. D., Tambe, S. S.,
Kulkarni, B. D. and Keshavamurty, R. N.,
Prediction of all India summer monsoon rainfall
using error-back-propagation neural networks,
Meteorol. Atmos. Phys., 62(1997) 225 -240.
Sahai, A. K., Soman, M. K. and Satyan, V., All
India summer monsoon rainfall prediction using
an artificial neural network, Climate Dynamics,
16(2000) 291-302.
PAI et al.: FORECAST OF DROUGHT/FLOOD MONSOON CONDITIONS-INDIAN SUBCONTINENT
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
Iyengar, R. N. and Raghu Kanth, S. T. G.,
Intrinsic mode functions and a strategy for
forecasting Indian monsoon rainfall, Meteorol.
Atmos. Phys., 90(2005) 17-36.
Mckee, T. B., Doesken, N. J. and Kleist, J., The
relationship of drought frequency and duration to
time scales, Eighth conference on Applied
climatology, Anaheim, CA, 1993.
Atri, S. D. and Ajit T., Met Monograph No 11,
Environment Meteorology-01/2010.
Pai, M. L., Pramod, K. V. and Balchand, A. N.,
Long range forecast on south west monsoon
rainfall using artificial neural networks based on
clustering approach, International Journal of
Information Technology and Computer Science,
6(2014) 1-8.
Heitzman, J. and Worden, R. L., India: A
Country Study, (Washington, D.C., The
Division) 1996.
www.esrl.noaa.gov/psd/data/gridded/data.coads.
1deg. html.
Woodruff, S. D., Worley, S. J., Lubker, S. J., Ji.,
Z., Freeman, J. E., Berry, D. I., Brohan, P., Kent,
E. C., Reynolds, R. W., Smith, S. R. and
Wilkinson, C., ICOADS release 2.5: extensions
and enhancements to the surface marine
meteorological archive, International Journal of
Climatology, 31(2011) 951-967.
Worley, S. J., Woodruff, S. D., Reynolds, R. W.,
Lubker, S. J. and Lott, N., ICOADS release 2.1
data and products, International Journal of
Climatology, 25(2005) 823-842.
Rajeevan, M., Bhate, J., Kale, J. D. and Lal, B.,
Development of a high resolution daily gridded
rainfall data for the Indian region, IMD Met.
Monograph Climatology 22/2005.
Nocke, T., Schumann, H. and Böhm, U.,
Methods for the visualization of clustered
climate data, Computational Statistics, 19(2004)
75-94.
Kovács, F., Legány, C. and Babos, A., Cluster
validity measurement techniques, 6th Int.
Symposium of Hungarian Researchers on
19.
20.
21.
22.
23.
24.
25.
26.
677
Computational Intelligence, Budapest, Hungary,
2005.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O.
P., Satbhai, N. S. and Mukhopadhyay, B.,
Development and analysis of a new high spatial
resolution (0.25° x 0.25°) long period (1901 –
2010) daily gridded rainfall data set over India,
NNC Research Report, RR No. 1/2003, IMD
Pune, 1 – 72.
Halkidi, M. and Vazirgiannis, M., Clustering
validity assessment: finding the optimal
partitioning of a data set, Proc. IEEE ICDM.,
2001 187-194.
Han, J., Kamber, M. and Pei, J., Data Mining:
Concepts and Techniques, 3rd ed. (Morgan
Kaufmann Series in Data Management Systems)
2011.
Sarkar, J., Datre, S., Thorat, G., Gore, S. D. and
Tyagi, A., Assessing drought scenario over India
during monsoon 2009 – an approach based on
standardized precipitation index, Asian Journal
of Water, Environment and Pollution, 8(2011)
47-59.
Dostál, P. and Pokorny, P., Cluster analysis and
neural networks, 17th Annual Conference
Proceedings, Prague, Czech Republic, 2008.
Kohail, S. N. and El-Halees, A. M.,
Implementation of data mining techniques for
meteorological data analysis (A case study for
Gaza Strip), International Journal of Information
and Communication Technology Research,
1(2011) 96-100.
Varikoden, H. and Preethi, B., Wet and dry years
of Indian summer monsoon and its relation with
Indo-Pacific
sea
surface
temperatures,
International Journal of Climatology, 33(2013)
1761-1771.
Singh, P. and Borah, B., Indian summer
monsoon rainfall prediction using artificial
neural network, Stochastic
Environmental
Research and Risk Assessment, 27(2013) 15851599.