This paper proposed a new approach for rainfall prediction method, which combines the Support Vector Machine (SVM) and Fuzzy Logic methods.The performance of the proposed method is compared to the Neural Network (NN)???Fuzzy.The climatological data is obtained from PT LAPAN Bandung and the Meteorology, Climatology and Geophysics Region IV Makassar Indonesiafor 10 years (2001-2010) and is analysed by MATLAB 7.6.The results confirm that the SVM-Fuzzy achieves higher accuracy than NN-Fuzzy

Achmad, Andani

Hasanuddin University Repository

Indrabayu, Nadjamuddin Harun, M.Saleh Pallu, Andani Achmad / International Journal of 
Engineering Research and Applications (IJERA) ISSN: 2248-9622   www.ijera.com 
Vol. 3, Issue 2, March -April 2013, pp.1805-1809 
1805 | P a g e  
A New Approach of Expert System for Rainfall Prediction Based 
on Data Series 
 
1
Indrabayu, 
2
Nadjamuddin Harun, 
3
M.Saleh Pallu, 
4
Andani Achmad 
1 Student of Doctoral Program Civil Engineering, Hasanuddin University 
1,2,4 Department of Electrical Engineering, Hasanuddin University 
3 Department of Civil Engineering, Hasanuddin University 
 
 
ABSTRACT 
This paper proposed a new approach 
for rainfall prediction method, which combines 
the Support Vector Machine (SVM) and Fuzzy 
Logic methods.The performance of the proposed 
method is compared to the Neural Network 
(NN)–Fuzzy.The climatological data is obtained 
from PT LAPAN Bandung and the Meteorology, 
Climatology and Geophysics Region IV 
Makassar Indonesiafor 10 years (2001-2010) and 
is analysed by MATLAB 7.6.The results confirm 
that the SVM-Fuzzy achieves higher accuracy 
than NN-Fuzzy. 
 
Keywords-Rainfall prediction, SVM-Fuzzy, 
Expert system and NN-Fuzzy. 
 
I. INTRODUCTION 
Rain is a natural phenomenon which has 
an important role in tropical countries like 
Indonesia.However, heavy rainfall may cause 
flooding and landslides that disturb the human 
activities, social economy, and the 
environment.Therefore, rainfall forecast is an 
essential and vital process nowadays. 
Several methods have been developed to 
predict the rainfall in recent years, such as Neural 
Network (NN), Fuzzy logic and Support Vector 
Machine(SVM).To determine the performance of 
those methods,  many studies have been conducted 
based on quantitative approach for rainfall 
forecasting [1]-[7].Therefore, a new approach of 
the rainfall prediction, which is called the SVM-
Fuzzy method,  is proposed in this paper which is 
more on qualitative based  rainfall prediction. 
In this paper, the rainfall data are 
obtainedfrom BMKG (Meteorology, Climatology 
and Geophysics Berau), region IV Makassar, 
Indonesia for 10 years (2001 - 2010) with predictor 
variables, such as wind speed (W), temperature (T), 
humidity (H) and Rainfall (R). 
The rest of this paper is organized as 
follows.In Section II, rainfall prediction methods 
are described.In Section III, a new approach of 
rainfall predictionis proposed.In Section IV, results 
and analysis are discussed.Finally, Section V 
concludes the paper and lists the futureworks. 
 
 
 
 
 
II. RAINFALL PREDICTION METHODS 
In this section, the two rainfall prediction 
methods are described.It comprises of algorithms 
and assumptions made in each method.  
 
II.1 Support Vector Machine (SVM) 
A Support VectorMachine (SVM) is a 
computer algorithm that learns by example to 
findthe best function of classifier /hyperplaneto 
separate thetwoclasses in the inputspace.The SVM 
analyzedtwo kinds of data, i.e. linearly and non-
linearly separable data [8].The example of linearly 
separated data is shown in 
Fig.1.Besthyperplanebetween twoclassescan be 
foundby measuring the hyperplanemarginand find 
out the maximumpoints.Margin is defined as the 
distance betweenhyperplaneand the closest pattern 
of each class, which is called support vector.The 
best hyperplaneis defined as the following 
equation. 
 
f(x) = wTx+ b(1) 
wherex refers to a training pattern, w is referred to 
as the weight vector and bas the  bias term. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig.1 The example of linearlyseparated data 
 
 
 
Margin 
Support vectors 
 
 
 
 
 
 
 
f(x) = 0 
Indrabayu, Nadjamuddin Harun, M.Saleh Pallu, Andani Achmad / International Journal of 
Engineering Research and Applications (IJERA) ISSN: 2248-9622   www.ijera.com 
Vol. 3, Issue 2, March -April 2013, pp.1805-1809 
1806 | P a g e  
II.2 Fuzzy Logic (Fuzzy) 
Fuzzy Logic is a type of reasoning based 
on the recognition that logical statements are not 
only true or false (white or black areas of 
probability) but can also range from “almost 
certain” to “very unlikely” (gray areas of 
probability).Fuzzy logic has proven to be 
particularly useful in expert system and other 
artificial intelligenceapplications. 
Fuzzy inference systems, also known as 
fuzzy rule-based systems or fuzzy models, 
areschematically shown in Fig.2.They are 
composed of five conventional blocks;arule-
basecontaining a number of fuzzy if-then rules, 
adatabasewhich defines the membershipfunctions 
of the fuzzy sets used in the fuzzy rules, adecision-
making unitwhich performs the inference 
operations on the rules, afuzzification 
interfacewhich transform thecrisp inputs into 
degrees of match with linguistic values, 
adefuzzification interfacewhichtransform the fuzzy 
results of the inference into a crisp output.Fig.3 
shows the example of temperature membership 
function. 
Fig.2 The general structure of a fuzzy inference 
system[9] 
 
 
 
 
 
 
 
 
 
Fig.3 The example of membership function 
 
 
 
II.3 Neural Network (NN) :Backpropagation 
The field of neural networks can be 
thought of as being related to artificial intelligence, 
machine learning, parallel processing, statistics, 
and other fields.The attraction of neural networks is 
that they are best suited to solving the problems 
that are the most difficult to solve by traditional 
computational methods. 
Most people would consider the 
backpropagationnetwork to be the quintessential 
Neural Network.Any network structure can be 
trained by backpropagationwhen the desired output 
patterns exist and each function has been used to 
calculate the actual output patterns is 
differentiable.The example of 
backpropagationnetwork is shown in Fig.4. 
 
Fig.4 The example of backpropagationnetwork 
 
III. THE PROPOSED SYSTEM 
The proposed system combines the SVM 
and Fuzzy methods to achieve high accuracy of the 
rainfall prediction.The input parametersof the 
proposed system are humidity (H), wind velocity 
(W), temperature (T), and rainfall (R) for 8 years 
data (2001-2008).The concept of the SVM-Fuzzy 
and its flowchartare shown in Fig.5.and Fig.6, 
respectively.The SVM-Fuzzy was simulated by 
Matlab7.6 in this paper. 
 
In SVM, eightyears data (2001-2008) 
prepared as training data aiming on 2009 
observation data.Re-trainingconducted over 2001-
2009 periods for predicting                 the humidity, 
wind and temperature in 2010.The input data are 
used in kernel Radial Basis Function (RBF) to 
determine the support vectors.The position of 
support vectors is neededto figure out the weight 
vector (w) and bias (b) as  predictionparameters. 
 
 
 
 
 
 
 
Indrabayu, Nadjamuddin Harun, M.Saleh Pallu, Andani Achmad / International Journal of 
Engineering Research and Applications (IJERA) ISSN: 2248-9622   www.ijera.com 
Vol. 3, Issue 2, March -April 2013, pp.1805-1809 
1807 | P a g e  
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Fig.5 The concept of SVM-Fuzzy 
 
In fuzzy, there are three input variables, 
one output and 36 rules.Humidity and temperature 
havethree membership functions, i.e. low, medium 
and high. While wind speed has four membership 
functions, i.e. calm, moderate, strong, very 
strong.The rainfallprediction as an output of the 
fuzzy system is classified into sunny, very light 
rain, light rain, moderate, heavy rain, and very 
heavy rain.The rainfall classifications are shown in 
Table 1. 
Table 1. Rainfall Classifications  
Rain type 
Rainfall (mm) 
1 hour 24 hours 
Very light < 1 < 5 
Light 1 s/d 5 5 s/d 20 
Moderate 5 s/d 20 20 s/d 50 
Heavy 10 s/d 20 50 s/d 100 
Very heavy > 20 > 100 
Fig.6  Flowchartof SVM-Fuzzy method 
 
IV. RESULTS AND DISCUSSIONS 
Ten years meteorological data (2001-
2010) are collected from BMKG Indonesia 
forMakassar City.The decision of having Makassar 
as sample city simply because Makassar lies in 
Monsoonal region. 
The prediction results of humidity, 
temperature, and wind speed of the SVM method in 
November 2009 are shownin Fig.7.to Fig.9, 
respectively. 
 
Fig.7  The prediction results of humidity 
 
Fig.8The prediction results of temperature 
 
Fig.9  The prediction results of wind speed     
From Figure 7-9, a quantitative 
meteorological daily prediction are generated by 
SVM to be fed into Fuzzy. It can be seen that 
prediction is quite accurate and also following the 
trend of actual data. 
From Figure 7 to 9, the SVM has shown 
great performance of predicting the next day 
parameter. The data trend between predicted and 
actual data are similar.  
The highintensity of the rainfall is 
obtained in Februaryand the results are shown in 
Fig.10 and Fig.11. 
 
Indrabayu, Nadjamuddin Harun, M.Saleh Pallu, Andani Achmad / International Journal of 
Engineering Research and Applications (IJERA) ISSN: 2248-9622   www.ijera.com 
Vol. 3, Issue 2, March -April 2013, pp.1805-1809 
1808 | P a g e  
 
Fig.10 Rainfall in February2009 
 
 
 
 
Fig.11 Rainfall in February2010 
Figure 11 shown the trend between2 
methods and actual data. NN-Fuzzy gives less 
accuracy in prediction which several days are 
predicted extremely differ with the actual data. 
SVM-Fuzzy has slight improvement compare to 
NN-Fuzzy, not only in better prediction but also in 
following the actual data trend. 
In order to compare the performances of three 
prediction methods, the prediction accuracy (A) is 
calculated as following equation. 
 
𝐴 =
𝑁𝑝
𝑁𝑡
 × 100%(2) 
where  Np is  the  amount of success prediction and 
Nt  is the total number of observations.The results 
are shown in Table 1, which confirms that 
theSVM-Fuzzy achieves higheraccuracy than NN-
Fuzzy.However, the accuracy is less than 90% due 
to using the daily data.The accuracy can be 
improved by using the hourly data.It was our 
assumption since short term prediction (daily) need 
a smaller time resolution too. 
 
Table 2.The accuracy of three prediction methods 
Prediction 
method 
Accuracy [%] 
2009 2010 
SVM – Fuzzy 75.62 62.47 
NN – Fuzzy 73.70 51.78 
 
Two methods are compared in Table 2 in term of 
percentage of accuracy. Slight improvement by 
SVM-Fuzzy in 2009 compare to NN-Fuzzy. 
However, a significant difference is found in 2010. 
This indicate that SVM-Fuzzy is better in 
predicting different phenomenon. This is due to the 
fact that in 2010 rain accoured in the whole year. 
 
V. CONCLUSION 
The proposed SVM-Fuzzy method for 
rainfall prediction was compared to the NN-
Fuzzy.The results show that the accuracy of  the 
proposed method is superior than NN-Fuzzy.For 
example, the accuracy of  the SVM-Fuzzy in 2009 
and 2010 are 1.92% and 10.69%  superior to that of 
the NN-Fuzzy, respectively. A better prediction in 
the future research will be expected. Since a new 
radar was installed at Hasanuddin University and 
the data sampling resolution (hourly) will gives a 
much better accuracy. 
 
REFERENCES 
[1] Indrabayu, Neural Network and Fuzzy 
methods for rainfall prediction, Proc.The 
1st FORTEI Conference, Makassar, 
Indonesia, 2011, pp135.(In Indonesian) 
[2] Indrabayu, N.Harun, M.S.Pallu, and 
A.Ahmad, Constructing Auto-Regressive 
Integrated Moving Average (ARIMA) as 
Expert Systemfor Daily Precipitation 
Forecasting, The 2nd MICEEI 
International Conference, Makassar, 
Indonesia, 2011, pp.89. 
[3] Indrabayu, N.Harun, M.S.Pallu, and 
A.Ahmad, Performanceof ASTAR for 
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3rdMICEEI, Makassar, Indonesia, 2012, 
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[4] Indrabayu, N.Harun, M.S.Pallu, and 
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1 4 7 10 13 16 19 22 25 28
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Indrabayu, Nadjamuddin Harun, M.Saleh Pallu, Andani Achmad / International Journal of 
Engineering Research and Applications (IJERA) ISSN: 2248-9622   www.ijera.com 
Vol. 3, Issue 2, March -April 2013, pp.1805-1809 
1809 | P a g e  
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A New Approach of Expert System for Rainfall Prediction Based on Data Series

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A New Approach of Expert System for Rainfall Prediction Based on Data Series

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