ANALYSIS AND PREDICTION OF RAINFALL USING MACHINE LEARNING TECHNIQUES

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1908
ANALYSIS AND PREDICTION OF RAINFALL USING MACHINE LEARNING
TECHNIQUES
Anurag Kumar1, Lalsingh Chouhan2
1Assistant professor, CSE, Dr. A.P.J. Abdul Kalam UIT Jhabua, Madhya Pradesh, India
2Assistant professor,CSE, Dr. A.P.J. Abdul Kalam UIT Jhabua, Madhya Pradesh, India
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Abstract - : Weather and climate prediction are
dominated by high dimensionality, interactions on many
different spatial and temporal scales, and chaotic dynamics.
Machine learning techniques can predict rainfall by
extracting hidden patterns from historical weather data. In
this technique apply the Multiple Linear regression (MLR)
and Support vector regression (SVR) model for rainfall
prediction. To design and implement the system, we have
gathered 115 years of data from 1901 to 2017 from Kaggle.
Our proposed model has been tested and validated with
respect to Multiple Linear regression and Support Vector
regression. Compared results reveal the satisfactory
performance, the SVR had provided maximum accuracy
Key Words: Rainfall prediction, Machine Learning,
Linear Regression, Support Vector Regression,
Accuracy
1. INTRODUCTION
Weather forecasting on the basis of historical data is a
complex but very helpful task. Which comes with several
problems that require to be solved in order to achieve
optimal result. Rainfall prediction is important all over the
world and it play a key role in human life. It’s difficult to
predict rainfall precisely with varying atmosphere
conditions. Accurate rainfall predictions are crucial for
several areas of society specially in agriculture. India is an
agricultural country and therefore the success of
agriculture depends of rainfall.Thereareseveral recourses
for water but in India agriculture is usually dependent on
rainfall. The weather has a significant impact on the
agricultural industry and because ofthat,havingtheability
to predict it helps farmers in their day-to-day decisions
such as how to plan efficiently, minimize costs and
maximize yields. The concept of machine learning is
getting used in every sector to reduce the labour cost and
increase the productivity. Every Machine learning
algorithm has three steps: Depiction, judgment,
development. Depiction guides us to represent the
discovered knowledge done fromthedata mining. Herewe
have used the two most popular machine learning
techniques to predict the rainfall. Those techniques are
Support Vector regression and Multiple Linear Regression
2. RELATED WORK
There are many works in the literature for the prediction
of rain fall. This section discusses some of the work related
to our proposed methodology.
Kumar Abhishek et al. have proposed a rainfall prediction
technique using neural network in [3]. The proposed
model in [3] predicts the rainfall of Udupi district from
Karnataka state of India. BPNN with feed forward, layer
recurrent and BPNN with cascade feed forward neural
networks are experimented. The proposed model takes
70% of the data for training and 30% for testing. The
recurrent network gives better accuracy when compared
to BPNN. The MSE is high in BPNN
Nasimul Hasan, Nayan Nath (2015) this paper exhibits a
robust rainfall prediction technique in view of recent
rainfall data of Bangladesh using Support Vector
Regression (SVR),a relapsemethodologyofSupportVector
Machine (SVM). It was challenging to make a 100 percent
perfect predictionandthedata waspreprocessedmanually
Linear Regression [1][12] is very useful for finding
relationship between two continuous variables, one is
independent variableandanotherisdependentvariable. In
Statistics, Linear regression refers to a model which show
relationship between two variables and how one can
impact the other. In Linear Regression, it shows how the
variation in the “dependent variable” can be captured by
change in the “independentvariables”.LinearRegressionis
statistical technique which used to generate insights on
consumer behaviour, understanding business and factors
influencing profitability. Linear regressions can be used in
business to evaluate the trends and make decision for
future. For example, if an organisation’s sales have
increased regularly every month for the last few years, by
conducting linear analysis on the sales data with monthly
sales, the company could forecast sales in future months.
We have used Multiple linear regression model, unlike
simple linear regression MLR has multiple independent
variables. SVR is a regression algorithm, so wecanuseSVR
forworkingwithcontinuousValuesinsteadofClassification
which is SVM [2]. In regression technique we try to
minimise the error rate while in SVR we try to fit the error
within a certain threshold.

to suit the algorithm [5]. The evaluation results of the
study conducted on the data shows that the projected
technique performs higher than the conventional
frameworks in term of accuracy and process running time
[5]. Approach yielded the utmost prediction of almost
99.92%.
G.Mahalakshmi and S.Sridevi (2016) presented a paper
which gives detailed survey of the various techniques
applied for forecasting different types of time series. This
survey covers the overall forecasting models, the
algorithms used within the model and other optimization
techniques used for better performance and accuracy [6].
The various performance evaluation parameters used for
evaluating the forecasting modelsarealsodiscussedinthis
paper [6]. This study gives the reader an idea about the
various researches that take place withinforecastingusing
the time series data.
Paper proposed by [7] introduced rainfall prediction
system using deep mining KNN technique. A single K value
is given which is used to find the total number of nearest
neighbours that helps to determine the class label for
unknown data. Similar parameters are clusteredintosame
type of cluster and thus with the helpof KNN wedetermine
the category of a specific datasets. This algorithm does not
require time for training of classification or regression.
This system may not lead to good accuracy if the incorrect
value of K is picked.
Sandeep Mohpatra and Animaka Upadhyay (2017)
presented a paper that focuses on use of data mining
techniques for predicting rainfall of an area on basis of
some dependent features like precipitation and wet day
frequency. They have collected data foryearsrangingfrom
1901 to 2002 of Bangalore, India [8].Theregression model
developed has been trained and validated against the
actual rainfall of that area. The performance of the
algorithm was further boosted using Ensembletechniques
using k-fold [8].
Chandrasegar, K S Harsha (2017) carried experiment on a
heuristic prediction of rainfall using machine learning
techniques. This paper discusses the rate of rainfall in
previous years according to various cropseasonslikeRabi,
Kharif and Zaid and predicts the rainfall in future seasons
[9]. Also, it measures the different categories of data by
linear regression method. Results help farmers to make
correct decision to harvest a particular crop according to
crop seasons. Linear regressionmethodsuggeststhelower
correlation between various crop seasons [9].
3. METHODOLOGY
In this paper we have used Multiple Linear regression
and Support Vector regression to predict the amount
of rainfall.
3.1 Machine Learning Model
The proposed method is based on the multiple linear
regression and support vector regression. The data for the
prediction is collected from the publicly available sources
and the 70 percentage of the data is for training and the 30
percentage of the data is used for testing. Figure 1
describes the block diagram of the proposedmethodology.
Multiple regression is used to predict the values with the
help of descriptive variables and is a statistical method. It
is having a linear relationship between the descriptive
variable and the output values. The following is the
equation for multiple linear regression:
Yi = β0 + β1x1 + β2x2 + ... βkxk + ε
Here we are using "k" for the number of predictor
variables and we have k+1 regression parameters Where,
β0 is constant term, β1 variable is coefficient of x1, β2
variable is coefficient for x2, βk is xk coefficient variable
and ε is error associated with predicted value. Support
Vector Regression (SVR) uses the same principle as SVM,
Figure 1. block diagram of the proposed
methodology

but only for regression problems. SVR works on the
principle of structural risk minimization from statistical
learning theory [2] and establishes a hyperplane that can
predict the distribution of data. The principle of the SVR
algorithm has a given set of input training data set {(Ai, Bi),
i = 1, 2,...,k}, xi ∈ RM , where Ai is the input 3-D vector, Bi ∈ R
is the response output data, and k is the number of
samplings. The optimal lineardecisionfunctioninthehigh-
dimensional feature space is expressed as follows:
f (xI) = ωA + b
where ω refers to weight vectors and b denotes the bias.
3.2 Data and Sources of Data
For this study data has been collected from the
publically available source Kaggle [11], it contains the
monthly rainfall of each state of India form Jan 1901toDec
2017.
3.3 Performance metrics
Mean Absolute Error:
MAE [10] is the average of the absolute differences
between the actual value and the model’s predicted value.
The bigger the MAE, the more serious the error is.
Mean Squared Error:
MSE or Mean Squared Error[10] is one of themostpopular
metrics for regression algorithms. It is simply the average
of the real value’s squared difference with the regression
model’s predicted value.
Mean Absolute Percentage Error:
MAPE or Mean Absolute Percentage Error [10] is the
average absolute difference between the actual value and
the value predicted by the model divided by the real value
Root Mean Squared Error:
RMSE or Root Mean Squared Error[10] is similar to MSE,
just the final value is square rooted and calculated the
square of errors in MSE.
R2 Error:
R² or Coefficient of Determination is a prevalent metric
[10]. R² uses two mean squared error calculations. While
the first is the mean square of each real value versus the
average of observations, the second is the mean squared
error of the actual value versus the predicted one.
4. RESULTS AND DISCUSSION
We have considered dataset from 1901 to 2017 of Bihar
state, India. Visualization from different graphs help us to
understand more about the data and drives us to decide
the next step to taken. It provides important perceptions.
Forecasting gives appropriate and reliableinputregarding
to present, past and future activities with definite
numerical and scientific methods. There are some steps
involved in predicting the numerical values for a specific
task. Initial step is to recognize the problem with complete
analysis and second is collecting the appropriate data to
analyze the problem for further estimation. After
estimation, compare the actual and estimated values with
necessary actions. The data is arranged in such a way that
rainfall is plotted according to year i.e., yearly counts of
rainfall shown in graph 1.

Graph 1. Overall monthly data plot of rainfall from 1901 to 2017
Graph 2: Stacked bar chart of each year

The stacked bar chart above depicts the rainfall of each
month for a particular year, across each month. we cansee
from the sorted overall bar heights that year 1987 has the
highest rainfall and 2010 has lowest.
Graph 3 show the minimum, maximumandmadianrainfall
in each month using box plot. It clearly indicates that,
 The rainfall in the months January, February,
March, April, November and December is very
less.
 The rainfall in the months May and October is
average.
 The rainfall in the months June, July, August, and
September are high compared to rainfall in
other months of the year.
We can see a seasonal effect with a cycle of 12 months.
Graph 3: Box Plot graph describing the rainfall in each month.
Graph 4. shows rainfall in each month from 1901 to 2017.
Graph 4: Monthly rainfall through history.

we divide the data into Train and Test Sets: Number of
entries (training set, test set): (982, 422) Now we compare
the MLR and SVR model to understand which model gives
better result. Splitting the dataset into trainandtestdata we
have taken 70% for training and 30 % for testing the model.
A total of 982 train data and 422 test data is used. Plotting
the scatter plot of actual and predicted rainfall we get the
following graphs. Graph 5 shows the scatter plot of actual vs
predicted rainfall using MLR model. In Graph 6 we can
clearly see the comparison between each actual and
predicted value.
Graph 5: Scatter Plot of Actual vs Predicted rainfall using MLR
Graph 6: Actual vs Predicted rainfall using MLR

Graph 7: Scatter Plot of Actual vs Predicted using SVR
Graph 8: Actual vs Predicted rainfall using SVR

Graph 7 shows the scatter plot of actual vs predicted
rainfall using SVR model.
In Graph 8 we compared each actual and predicted value.
Table 1 Comparison of MLR and SVR Performance
S.
No.
matrices LR SVR
1 Train score 0.827149236683498 0.828585087676299
2 Test score 0.811790903215551 0.836907038442013
3 MAE 0.249229855446679 0.248383868743224
4 MSE 0.154636268612835 0.151737104047022
5 RMSE 0.393238183055556 0.389534470935528
6 MAPE 0.674927107614465 0.614009940253406
7 R2 Score 0.833790903215551 0.836907038442013
5. CONCLUSION
Here, we are using time series analysis to predict therainfall
using monthly rainfall from year 1901 to 2017. For yield to
accuracy, machine learningalgorithmssuchasMLR andSVR,
were implemented and tested on the givendatasetsfromthe
Bihar states. Both algorithms are compared with their
accuracy. Comparing the different performancematrices,we
can conclude that SVR accuracy is better than MLR.
REFERENCES
[1] Amanpreet Singh , Narina Thakur , Aakanksha Sharma
“A review of supervised machine learning algorithms”,
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[2] Mariette Awad, Rahul Khanna, “Support Vector
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[3] Kumar Abhishek,AbhayKumar,RajeevRanjan, Sarthak
Kumar, “A Rainfall Prediction Model using Artificial
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(ICEI)

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