Download Rainfall Prediction with TLBO Optimized ANN *, K Srinivas B Kavitha Rani

Journal of Scientific & Industrial Research
Vol. 73, October 2014, pp. 643-647
Rainfall Prediction with TLBO Optimized ANN
a
a
B Kavitha Rani *, K Srinivas and A Govardhanb
a
Jyothishmathi Institute of Technology & Science, Karimnagar, Andhra Pradesh, India
b
Department of CSE & Director, School of IT
JNTUH University, Hyderabad
Received 9 January 2014; revised 3 June 2014; accepted 1 August 2014
Rainfall prediction is very crucial for India as its economy is based on mainly agriculture. The parameters that are
required to predict the rainfall are very complex in nature and also contain lots of uncertainties. Although various
approaches have been earlier suggested for prediction, the soft computing is found to be very effective in developing models
which emulates human being and derives expertise like human being to adapt to the situations and learn from the
experiences. In this study, rainfall prediction for Andhra Pradesh (AP) state is carried out with Artificial Neural Network
(ANN). A new heuristic approach Teaching Learning Based optimization (TLBO) is used to train the weights of the ANN
developed for rainfall prediction. A comparison is done with classical back Propagation learning approach and mTLBO
(a variant of classical TLBO). The data of monthly rainfall (mm) in Coastal Andhra is collected from Indian Institute of
Tropical Meteorology (IITM), Pune, India. The data set consists of 1692 monthly observations during years 1871 to 2011.
The simulated results reveal the effectiveness of ANN-mTLBO over ANN-BP on investigated datasets. The findings of our
work will be very useful in assessing the possible drought situations in AP from the rainfall predictions.
Keywords: Rainfall Predictions, ANN, TLBO, Back Propagation
Introduction
In India the entire agriculture depends upon
rain. The economy of India is mainly centered on the
productivity from the agricultural outputs. It is
thus a major concern to identify any trends for
rainfall to deviate from its periodicity, which would
disrupt the economy of the country. Even a short term
prediction of rainfall is highly difficult due to the fact
that parameters involved in predicting rainfall are
very complex and uncertain. Rainfall-runoff3
processes
are
non-linear
complex
systems
involving several contributing factors such as
rainfall depth, rainfall distribution, land use, soil type,
soil moisture content, etc. Due to process and
model complexity, these models are often fitted
without serious consideration of parameter
values, resulting in poor performance during
verification1.Another problem with both conceptual
and physically-based models is that empirical
regularities or periodicities are not always evident and
can often be masked by noise 2.
——————
*
Author for Correspondence
Email: [email protected]
Artificial neural networks
In this work ANN based rainfall prediction model
is proposed with a recently developed heuristic
algorithm known as teaching-Learning based
optimization (TLBO)8,9. The weights of the ANN
developed in the work is trained with TLBO
technique. TLBO is a population based approach
which starts with many candidate solutions and
eventually achieves the desired optimum target with
iterations. Unlike back propagation algorithm TLBO
does not get trapped in local optima. In this work an
exhaustive simulations are carried out with TLBOANN and BP-ANN for rainfall detection4-7 of AP
state, mainly coastal Andhra Pradesh. ANNs are
mathematical models with a highly connected
structure inspired by the structure of the brain and
nervous systems. ANN processes operate in parallel,
which differentiates them from conventional
computational methods. ANNs consist of multiple
layers - an input layer, an output layer and one or
more hidden layers as shown in Figure 1. Each layer
consists of a number of nodes or neurons which are
inter-connected by sets of correlation weights. The
input nodes receive input information that is
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J SCI IND RES VOL 73 OCTOBER 2014
Teaching-Learning Based Optimization
This optimization method is based on the effect
of the influence of a teacher on the output of
learners in a class. Like other nature-inspired
algorithms, TLBO is also a population based method
that uses a population of solutions to proceed to the
global solution. A group of learners are considered as
the population. In TLBO, different subjects
offered to learners are considered as different design
variables for the TLBO. The learning results of a
learner is analogous to the ‘fitness’, as in other
population-based optimization techniques. The
teacher is considered as the best solution obtained so
far. There are two parts in TLBO: ‘Teacher Phase’
and ‘Learner Phase’. The ‘Teacher Phase’ means
learning from the teacher and the ‘Learner
Phase’ means learning through the interaction
between learners.
Teacher phase
Figure 1—The structure of ANN
processed through a non-linear transfer function to
produce outputs to nodes in the next layer. These
processes are carried out in a forward manner hence
the term multi-layer feed-forward model is used. The
learning or training process uses a supervised learning
algorithm that compares the model output to the
target output and then adjusts the weight of the
connections in a backward manner. The process can
be summarized in mathematical form as follows.
… (1)
where Xo and Woj are the bias (Xo = 1) and its bias
weight, respectively. N represents the number of input
nodes. Each hidden node input (netj) is then
transformed through the non-linear transfer function
to produce a hidden node output, Yj. The most
common form of the transfer function is a sigmoid
function and is expressed as follows:
… (2)
Similarly, the output values between the hidden
layer and the output layer are defined by
… (3)
where M = the number of hidden nodes; Wjk = the
connection weight from the j-th hidden node to the
k-th output node; and Zk = the value of the k-th
output node.
In our society the best learner is mimicked
as a teacher. The teacher tries to disseminate
knowledge among learners, which will in turn
increase the knowledge level of the whole class and
help learners to get good marks or grades. So a
teacher increases the mean learning value of the class
according to his or her capability i.e. say the
will try to move mean
towards their
teacher
own level according to his or her capability, thereby
increasing the learners’ level to a new mean
.
will put maximum effort into teaching his
Teacher
or her students, but students will gain
knowledge according to the quality of teaching
delivered by a teacher and the quality of students
present in the class. The quality of the students is
judged from the mean value of the population.
Teacher
puts effort in so as to increase the
quality of the students from
to
, at which stage
the students require a new teacher, of superior
quality than themselves, i.e. in this case the new
teacher is . Let
be the mean and
be the
teacher at any iteration . will try to move mean
towards its own level, so now the new mean will be
.The solution is updated
designated as
according to the difference between the existing
and the new mean given
… (4)
where
is a teaching factor that decides the value of
mean to be changed, and is a random number in the
range [0, 1]. The value of
can be either 1 or 2,
KAVITA RANI et al.: RAINFALL PREDICTION WITH TLBO OPTIMIZED ANN
which is again a heuristic step and decided randomly
with equal probability as
… (5)
This difference modifies the existing solution
according to the following expression
… (6)
Learner phase
Learners increase their knowledge by two
different means: one through input from the teacher
and the other through interaction between themselves.
A learner interacts randomly with other learners
with the help of group discussions, presentations,
formal communications, etc. A learner learns
something new if the other learner has more
knowledge than him or her. Learner modification is
expressed as
Randomly select two learners Xi and Xj; where ij
… (7)
Accept Xnewif it gives a better function value
Numerous modifications of Teaching Learning
Based Optimization (TLBO) algorithm have been
done for improvement of algorithm. In our work we
have used a variant of TLBO known as mTLBO8 for
our simulation purpose. A brief description is given
below for the same.In this modification of TLBO only
the Learner phase of basic TLBO is modified.
The Teacher phase remains same as in TLBO.
Through the exhaustive analysis of TLBO concept it
is clearly evident that more the learner is learned
better the solution. In a traditional teaching-learning
environment the learners output is dependent on the
interaction between learners and the class room
delivery by teachers. To further enhance the learning
capability of students an extra training through the
tutorial helps. A learner interacts randomly with other
learners with the help of group discussions,
presentations, formal communications, etc. and at the
645
same time he or she can discuss more closely
with the teacher who is more knowledgeable
person in a tutorial class. A learner always
learns something new form the teacher and if the
other learner has more knowledge than him or her
then only he or she gets more knowledge. Hence,
an extra term is added in the learner phase equation to
modify TLBO.
Now the New Learner modification is expressed
as in place of its corresponding equation of
basic TLBO
… (8)
The third term in the above equation (8) represents
the close interaction between a teacher and learner
analogous to tutorial concept.
Experimental Set Up and Simulations
1 The model building process consists of four
sequential steps:
2 Selection of the input and the output data for the
supervised BP/TLBO learning.
3 Normalization of the input and the output data.
4 Training of the normalized data using BP/TLBO
learning.
5 Testing the goodness of fit of the model.
6 Comparing the predicted output with the desired
output
Dataset Description
The data of monthly rainfall (mm) in Coastal
Andhra is collected from Indian Institute of Tropical
Meteorology (IITM), Pune, India. The data set
consists of 1692 monthly observations during years
1871 to 2011. In this study, to rescale the variables
adjusted normalized technique is used. These adjusted
values fall in range from -1 to +1. The used data is
split into three different samples such as training
sample, testing sample and hold out sample.
Structure of the ANN
The model proposed uses three layer feed forward
neural network with input layer, one hidden and one
output layer. To develop a very simple ANN a single
hidden layer is chosen. The number of input neurons
needed by model is two, each representing the values
of lag12 (monthly rainfall of previous year) and
Month (where month takes the values 1 to 12 for
January to December respectively). The training
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J SCI IND RES VOL 73 OCTOBER 2014
dataset is grouped year and month wise. The input
data set is a matrix with two columns and rows equal
to the size of the training dataset. The predicted
rainfall of a month is a function of the corresponding
month of previous years available in the training
dataset. For example predicted rainfall of 2011
January is the function of all previous years January
month rainfalls. The model uses only one output unit
which indicates the forecast of monthly rainfall.
Number of neurons in hidden layer is initially started
with one neuron and based on the RMSE of the model
the number of neurons are grown. After several
simulations we find that the model provided fairly
good results with seven neurons in the hidden layer.
This demonstrates the use of Forward selection
method to determine rainfall predictions. The
model performance is shown in table 1 by calculating
Root-Mean-Square Error (RMSE) which is used to
measure how close forecasts or predictions are to the
eventual outcomes given in the data.The Figure 2
below demonstrates the predicted values with the
target values of both ANN-BP and ANN-mTLBO. It
can be clearly observed that the ANN-mTLBO
outperforms ANN-BP in terms of predicted accuracy.
The RMSE shown in table 1 is the validation of the
better predicted accuracy of ANN-mTLBO. The entire
simulation was carried out with standard BP
algorithm and the parameters of mTLBO were chosen
from8. Since the TLBO is a population based
Table 1—Model performance
Data set
In-Sample set
Out-of-Sample set
In sample Set
Out of sample set
Models
RMSE
ANN-BP
ANN-BP
ANN-mTLBO
ANN-mTLBO
0.47
0.53
0.23
0.33
algorithm several simulations are taken to average the
RMSE presented in the work. It is evident from the
results analysis that mTLBO is not trapped in local
optimum solution unlike BP algorithm. It may be
reiterated here that BP algorithm has the deficiency to
be get trapped to local optima solution as it is gradient
based. Whereas the population based approach of
mTLBO provides the algorithm to seek the better
solutions region after several iteration and provides
optimized weights and bias values for the chosen
ANN model.
Conclusion and future improvements
The objective of this work is to predict rainfall
in the AP state by using a suitable ANN model. As
ANN is better non-liner function approximate
this work has once aging emphasized the well
researched ANN model. In this work a suitable model
for rainfall detection in Coastal Andhra is
developed using both back propagation and TLBO
algorithm. A very minimal ANN model selected with
forward selection mechanism consisting of one
hidden layer with seven neurons are trained separately
using BP and a variant of TLBO known as mTLBO.
The results obtained from simulations reveal the
accuracy of ANN-mTLBO over ANN-BP. The
performances are compared with RMSE values and
the graph of predicted and targeted outputs is shown
as comparison for both approaches. The findings of
the models developed can be very helpful to identify
the drought situations in the regions of Andhra
Pradesh and suitable steps can be initialed by the
government
organizations
to
mitigate
the
disadvantages. The study can be further be enhanced
taking several other parameter as inputs for rainfall
predictions.
Figure 2—Figure showing comparison between original and predicted rainfall of Coastal Andhra years (2007-2011)
KAVITA RANI et al.: RAINFALL PREDICTION WITH TLBO OPTIMIZED ANN
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