Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

Abstract

The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues, this paper proposes an enhanced and reliable weather forecasting technique. As well as developing weather forecasting in remote areas. Weather data analysis and machine learning techniques, such as Gradient Boosting Decision Tree, Random Forest, Naive Bayes Bernoulli, and KNN Algorithm are deployed to anticipate weather conditions. A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting. The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible. Experimental evaluation shows our ensemble technique achieves 95% prediction accuracy. Also, for 1000 nodes it is less than 10 s for prediction, and for 5000 nodes it takes less than 40 s for prediction.

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Computers, Materials & Continua
DOI: 10.32604/cmc.2022.030067
Article
Weather Forecasting Prediction Using Ensemble Machine Learning for Big
Data Applications
Hadil Shaiba1, Radwa Marzouk2, Mohamed K Nour3, Noha Negm4,5, Anwer Mustafa Hilal6,*,
Abdullah Mohamed7, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6and
Mohammed Rizwanullah6
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman
University, Riyadh, 11671, Saudi Arabia
2Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint
Abdulrahman University, Riyadh, 11671, Saudi Arabia
3Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University,
Saudi Arabia
4Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5Faculty of Science, Mathematics and Computer Science Department, Menoufia University, Egypt
6Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University,
AlKharj, Saudi Arabia
7Research Centre, Future University in Egypt, New Cairo, 11845, Egypt
*Corresponding Author: Anwer Mustafa Hilal. Email: a.hilal@psau.edu.sa
Received: 17 March 2022; Accepted: 19 April 2022
Abstract: The agricultural sector’s day-to-day operations, such as irrigation
and sowing, are impacted by the weather. Therefore, weather constitutes a
key role in all regular human activities. Weather forecasting must be accurate
and precise to plan our activities and safeguard ourselves as well as our
property from disasters. Rainfall, wind speed, humidity, wind direction, cloud,
temperature, and other weather forecasting variables are used in this work for
weather prediction. Many research works have been conducted on weather
forecasting. The drawbacks of existing approaches are that they are less
effective, inaccurate, and time-consuming. To overcome these issues, this paper
proposes an enhanced and reliable weather forecasting technique. As well
as developing weather forecasting in remote areas. Weather data analysis
and machine learning techniques, such as Gradient Boosting Decision Tree,
Random Forest, Naive Bayes Bernoulli, and KNN Algorithm are deployed
to anticipate weather conditions. A comparative analysis of result outcome
said in determining the number of ensemble methods that may be utilized
to improve the accuracy of prediction in weather forecasting. The aim of
this study is to demonstrate its ability to predict weather forecasts as soon
as possible. Experimental evaluation shows our ensemble technique achieves
95% prediction accuracy. Also, for 1000 nodes it is less than 10 s for prediction,
and for 5000 nodes it takes less than 40 s for prediction.

3368 CMC, 2022, vol.73, no.2
Keywords: Weather; forecasting; KNN; random forest; gradient boosting
decision tree; naive bayes bernoulli
1Introduction
In Meteorological data, a huge volume of data is collected by various sensors for the prediction
of weather forecasting. Gathering such big data is a vital process that aids people’s day-to-day
activities. Prediction of weather conditions is essential for human beings to plan mitigation measures
for them from harmful weather conditions. It is also useful in many areas, including decision-
making, agriculture, tourism, and business [1,2]. Big Data contains tremendous weather information
in an unstructured, semi-organized format. Handling this unstructured data is a difficult task to
process and store. Therefore, a machine learning technique is implemented. The current prediction
of weather forecasting models depends on complicated physical models which need high-performance
computer systems with many HPC nodes for the implementation of the prediction systems. Despite
the employment of high-performance computers and high-speed communication lines, such systems
yield erroneous forecasts or an incomplete understanding of atmospheric processes. Furthermore,
running such a sophisticated model takes more time [3]. To address the drawbacks of existing models,
the proposed method employs a variety of algorithms and ensembles them using a maximum voting
mechanism. It is reliable, accurate, and has a minimum prediction time and error rate.
Machine learning methods are also used in weather forecasting classification. There is machine
learning techniques exist that have the ability to predict the weather conditions such as rainfall, wind
speed, wind direction, and temperature. Random forest, KNN, Gradient boost algorithm, decision
tree, and other machine learning techniques are combined to build a machine learning algorithm,
which is referred to as an ensemble-based technique. The advantage of using the ensemble-based
technique is to increase the prediction accuracy and produces better results.
The main contributions of our proposed ensemble-based weather forecasting (EBWF)model
are:
•An ensemble-based prediction technique is proposed for enhanced weather prediction perfor-
mance.
•Gradient boosting technique is developed for identifying relevant features for accurate weather
prediction. This feature selection approach requires minimum time complexity.
This article is organized as follows: Section 2 provides a review of several research literature,
Section 3 describes the prediction of weather forecasting using an ensemble-based approach of max
voting, Section 4 discusses the results, and Section 5 concludes the research with future directions.
2Literature Review
The use of classical and deep learning algorithms to forecast weather temperature has been
widely investigated in the literature. The majority of the work relies on supervised learning techniques.
Historical data of meteorological factors such as past temperature and wind speed and direction are
used to forecast the weather. The support vector machine (SVM) and artificial neural network (ANN)
are the most common models used in weather forecasting so far. The capacity of CNN to deal with
non-linear and high-dimensional data is well-known. SVM is noted for its accuracy and resilience.
Based on previous studies, prior temperature, relative humidity, solar radiation, rain, and wind speed
observations are the top attributes for forecasting temperature among the available meteorological
attributes. Among the most commonly utilized performance measurements are Mean Squared Error

CMC, 2022, vol.73, no.2 3369
(MSE) and Mean Absolute Error (MAE). Notice that some models are dedicated to predicting hourly
temperatures whereas others predict longer-term temperatures such as 24h ahead [4].
The authors in [5] released a publicly available weather dataset and conducted simple models as
a baseline enabling others to run new models and compare their findings with the existing models
to further research in weather prediction up to several days ahead. The dataset includes weather data
between 1979 and 2018. The dataset includes features such as temperature, humidity, wind, cloud cover,
precipitation, and solar radiation. In addition to constant variables such as the soil type, longitude,
and latitude. Regression, deep learning, and physical prediction models are among the baseline models
presented in the study. The outcomes of the experiments are reported using several performance
metrics. When dealing with weather forecasts, the authors recommend utilizing a successive period
of time for testing and validating the models rather than using random samples of data. Therefore, the
authors used the year 2016 for validating the performance of their models, the years 2017 and 2018 for
testing, and all years from 1979to 31 December 2016 for training the models. The authors mentioned
some challenges and future directions for research and that included, selecting the best combination
of features, applying different machine learning techniques, dealing with big data, and having larger
weather datasets to improve weather forecasting.
The WRF model (Weather Research and Forecasting) is a complex numeric weather prediction
model. Short-term and long-term prediction models were implemented using the deep learning models
named long short-term memory (LSTM) and temporal convolutional network (TCN) [6]. The results
are compared with the WRF model. LSTM and TCN models were first implemented for short-
term weather forecasts. They were then fine-tuned for long-term predictions. The study proposes two
different forms of models. The first model employs a single network with 10 inputs and outputs. That is,
it predicts future values of all-weather predictors based on their historical values. The second model, on
the other hand, implies 10 separate networks, each with ten inputs and only one output. This indicates
that each network receives historical values for all-weather attributes, but only forecasts the future
value of one attribute at a time. The training dataset includes the duration from January to May 2018.
The testing data covers the period of June 2018 while the validation set includes the period of July
2018. The results show that proposed deep learning-based models outperform the WRF model with
the advantage of having a lightweight model compared to WRF. They also show that having a network
for each prediction produces better results than having one network that produces all the forecasts.
The proposed model in [7] combines numerical weather prediction (NWP) with historical data to
forecast the weather. The study uses deep learning called the deep uncertainty quantification model
(UQM) and optimizes its performance by using a loss function that is based on the negative log-
likelihood error (NLE). The data is pre-processed. First, records with entirely missing data are deleted
otherwise linear interpolation is used to impute missing data. Continuous features are normalized
using min-max normalization into [0, 1]. Categorical data are encoded by embedding. Three weather
features are used in the proposed model which are the temperature and relative humidity at 2 meters
and wind at 10 meters. Tab. 1 describes that survey on the prediction of Weather forecasting.

3370 CMC, 2022, vol.73, no.2
Table 1: Survey on attributes and prediction techniques of weather forecasting
Author Attributes Technique Prediction
Abdel-Kader et al. [1]
(2021)
Temperature, humidity,
wind speed
Multilayer perceptron
with particle swarm
optimization
Prediction of rainfall
Basha et al. [8], (2020) Temperature, humidity,
wind speed, wind
direction
ANN, SVM, MLP
Model, Auto-Encoders
Prediction of rainfall
Huang et al. [9] (2020) Temperature, humidity,
air pressure
LSTM, MLP Prediction of air
pressure
Mohammed et al. [10]
(2020)
Measurement of
Rainfall
Multi-Linear support
vector regression and
lasso regression
Prediction of rainfall
Suresha [11] (2020) Humidity, Vapour
Pressure
K-means clustering,
Linear Regression,
Multivariate Multiple
Linear Regression
Prediction of rainfall
Ye n e t a l . [ 12] (2019) Humidity, pressure,
wind speed and
direction, temperature,
rainfall, sea level,
Echo state-based
network (ESN), Deep
Echo state-based
network (Deep ESN)
Prediction of rainfall
Singh et al. [13] (2019) Temperature, humidity,
and pressure
Random forest Prediction of rainfall
Parashar [14] (2019) Dust particles, pressure,
humidity, rainfall,
temperature.
Multiple linear
regression
Prediction of
Temper at ure
Mahabub [15] (2019) Rainfall, humidity,
temperature, and wind
speed,
Support vector and
linear regression,
bayesian ridge,
Gradient Boosting
(GB), Extreme GB
Prediction of
rainfall, wind speed.
3Proposed Methodology
The proposed framework has been developed with all components including weather data collec-
tion, pre-processing, feature selection, ensemble-based evolutionary model building, and evaluation of
the prediction results. Fig. 1 explains the weather forecasting proposed framework. Ensemble learning
is an advanced machine learning technique that combines the results of several machine learning
algorithms. This will lead to a better prediction of weather forecasting compared to single machine
learning algorithms.

CMC, 2022, vol.73, no.2 3371
Figure 1: Weather forecasting framework
The weather data set is divided into training data and testing data. The data is pre-processed to fill
missing values and perform data normalization. The features are identified using gradient boosting
which uses a gradient descent algorithm. The selected features are then classified using ensemble
machine learning (ML) algorithms such as random forest, gradient boosting decision tree, Naive Bayes
Bernoulli, and k-nearest neighbor (KNN) Algorithm to predict weather conditions. The prediction
results of the above algorithms are ensemble using the bagging method which is called max-voting for
the final prediction result.
3.1 Data Collection
The data is collected from various airport weather stations in India [16–18]. This dataset includes
various attributes of air temperature, atmospheric pressure, humidity, the direction of the wind,
and other variables. The sample attribute of weather data is given in Tab. 2. The experiments are
implemented using Python version 3.7.3 with the Tensorflow version. The dataset includes contains
2006–2018 with 9 Features. Our model forecasts the weather 3 h ahead of time. The training dataset
includes all features from the year 2006-to 2016. The testing dataset includes the years 2017 and 2018
with selected features in the dataset. Fig. 2 shows the feature representation of the weather data set in
every 3 h which contains 27 features.
Table 2: Sample weather data set attributes
Attributes Upper bound Lower bound
f1_Longitude 130.87 33.51
f2_Latitude 37.43 33.51
f3_Height
(Continued)

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Table 2: Continued
Attributes Upper bound Lower bound
f4_ wind direction for 10 min (0.1 deg) 3500 0
f5_ wind velocity for 10 min (0.1 m/s) 423 0
f6_temperature for 1 min (0.1 C) 498 −398
f7_ humidity for 1 min(0.1%) 1000 0
f8_atmospheric pressure for 1
min(0.1 hPa)
10907 0
f9_ pressure in sea level (0.1 hPa) 11163 0
Figure 2: Feature representation for every 3 h
3.2 Pre-Processing
The collected weather dataset contains invalid or empty values. Therefore, the pre-processing stage
is necessary. This stage includes data cleaning, data normalization, and one-hot encoding. Fig. 3 shows
the phases of pre-processing.
Figure 3: Phases in pre-processing
In data cleaning, raw weather data contains noise, inconsistent values, and missing values which
affect the accuracy of the weather forecasting. In order to improve the quality and performance of
the result. The null values are identified and eliminated from the data set. In this work, Min-Max
normalization is used. The weather dataset is scaled into a range normalization] or [−1, 1]. This Min-
Max normalization meta attributed the input of the attribute values o dnorm and the range of val is
between [min,max]by using the following formula:
dnorm =(max −min)∗(d−low)
high −low (1)
Here max is the maximum value of the selected attribute and min is the minimum value of the
selected attribute. dnorm is the newly selected feature after applying the normalization. The benefit of
normalization is to have data consistency.
One hot encoding converts the categorical feature of wind direction and its condition with dummy
variables. This conversion is needed in the training and testing data set for keeping the same number of

CMC, 2022, vol.73, no.2 3373
attribute features. That is by giving 1 for the presence of attribute and 0 for the absence of the attribute
in the dataset.
3.3 Feature Selection
There are various feature selection algorithms like information gain, correlation, gain ratio, and so
on. Such algorithms are used to identify important features from the whole feature set. This proposed
concept uses an ensemble learning method called gradient boosting for relevant features selection. The
bootstrap samples are independent and distributed evenly with the minimum correlation between the
sample weather data. The gradient boosting technique uses gradient descent steps to reduce the loss
while including input data into the ensemble model. Gradient descent is similar to the random forest
but different in the way that samples are chosen without replacement.
Algorithm 1: A bootstrapped gradient boosting
Input: Pre-processed dataset
Output: Selected features
Step 1: Randomly select the features subset from the pre-processed features.
Step 2: for i =1to d // training data sample
Step 3: For all iteration iter the gradient boosting generates the new subset which includes an estimator
called eto create the better subset model. The eis represented as,
F(t+1)(d)=Ft(d)+e(d)=Y(targ)(2)
e(d)=y−Ft(d)(3)
Step 4: The calculated weight after each iteration in the Eq. (4)
wei=−ηδC
δwei
(4)
where cost function C(We)=1
2
i
(targi−outi)2and η-Learning rate which is updated over time as,
η(t+1)=η(t)
(1+t×d)where d–decreasing constant
Step 5: update weight as
wei:=wei+wei(5)
Step 6: end for
Step 7: Selecting the relevant feature from the dataset is based on the rank order of weight value in
descending order. The top highest weight value is selected as relevant sub-features. After implementing
Algorithm 1, it selected important features from the whole set of features in the dataset every 3 h. The
selected important features are f4, f5, f6, f7, f8 were selected from the dataset.
3.4 Ensemble Learning Method
Ensemble methods are a combination of various algorithmic models. In our proposed model, we
combine the results of Random Forest (RF) [19], Gradients Boosting Decision Tree (GBDT), Naïve
Bayes Bernoulli (NBB), and KNN Algorithmic model. The final prediction outcome uses the outcome
of the above algorithms which are then ensemble by max voting to produce a better result.

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3.4.1 Random Forest
The random forest is a decision tree ensemble method in which weather data samples are classified
based on many sub-trees. A classification outcome is generated for each tree which are then ensemble.
Random Forest is similar to decision trees,
t(x,θn)for n =1,2...N(6)
where θn-is an independent and identical distribution of random samples. Each prediction class K trees
is given to a data sample D. The construction of the subset of decision tree is based on the following
formula,
D={(xi,yi)}(|X|=n,xi∈Rm,yi∈R(7)
The prediction probability of each subset is as follows,
prob (pc|F)=pc1+pc2+...+cpn
n

i=1
(pci(pc|F)) (8)
where pc-is the prediction class, F-are the features, pc1, pc2,...., and pcnis the probability of each
feature in the dataset, nis the number of subsets, and the error is calculated as,
err ≤corr 1−s2
s2(9)
Here corr is the correlation between the trees and sis the parametric strength of the tree. The
random forest classifier is shown in Fig. 4. In the training dataset, nis the subset of the decision tree
which is partitioned and a prediction is calculated for every tree. In Fig. 3 the prediction of each tree
is shown in different colour. As a whole, the average of all subsets of the trees’ prediction result is
considered as the final prediction class of the random forest.
Figure 4: Random forest

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3.4.2 Gradient Boosting Decision Tree (GBDT)
This approach works like machine learning techniques and effective in the prediction of weather
forecasting. It gathers large volume of weak models and changes the modules’ weight sample in every
single step. GBDT is an ensemble boosting technique that iteratively generates decision trees based
on its new regression value. This newly generated regression decision tree is used to fit the error in the
classification of weather input dataset in every step of the process. The purpose of using GBDT is, it
works with non-linear complex variable. It performs better in the training stage than the testing stage
because it has no overfitting problems. The probability is calculated by logarithm of ratio between
known feature attribute values to the unknown feature attribute data set. it can be defined as:
Pn(di)=
N

n=1
γndtn(di)(10)
Here Pn(di)is the probability of attributes in the weather forecasting model and it is evaluated
from the N. regression trees dtn(di)and γnis the step length. At each iteration of the process, a weak
decision tree dt (di)is selected to reduce the loss function loss [yi,Pn(di)].hereyiis a value of observing
di. Now the equation is formed as,
Pn(di)=Pn−1dtn(di)+γndtn(di)(11)
Pn(di)=Pn−1dtn(di)+arg min
n

i=1
loss [yi,Pn−1(di)−dtn(di)](12)
The gradient loss function can be defined as residual error between yiand Pn−1.dtn(di)is expressed
as:
Pn(di)=Pn−1(d)+γn
n

i=1∇loss [yi,Pn(di)](13)
In all iteration, a new weak module is created with respect to the last module-based errors. The
GBDT module trains the weather data and produces a better prediction with low noise of the data.
3.4.3 Naive Bayes Bernoulli (NBB)
To improve the accurate analysis of weather forecasting Naïve Bayes technique is implemented.
This technique is based on the concept of occurrence probability. And also, it shows the accurate
outcome using attribute of the weather dataset and it receives the primitive process. The Bayes theorem
is used in the NBB model and is defined as shown below:
prob (X|Y)=Prob (Y|X)prob (X)
Prob (Y)(14)
In Eq. (14), the probability of Y is the evidence and X is the hypothesis. Here X and Y are the
events. For finding the probability of X’s occasion, the Y’s occasion is a valid one. Then Y’s occasion
is termed as proof. The probability of X is priori of X. Similarly, the probability of prob (X|Y)is
deduced by Y.
3.4.4 KNN
KNN algorithm can be used for both predictive problems and classification of weather forecast-
ing. Its analysis the weather input vector values of prediction values and observation values to generate
the new set of data points. In the prediction of the weather condition [20,21], it uses a series of input
data with different nearest neighbour values. In the weather attribute missing attribute, values are

3376 CMC, 2022, vol.73, no.2
evaluated based on the similarity of attributes by using distance function. This paper uses Euclidean
distance of each data from weather input data vector by using the following equation:
Dist (p,q)=



n

i=1
(pi−qi)2(15)
Here P={p1,p2,...,pn}and Q={q1,q2,...., qn}. The purpose of using KNN algorithm
is predicting both numerical and categorical attribute values of weather dataset, missing values can
be easily identified and also correlated data also considered. Notice that it is time consuming in the
analysis of Big-data process.
3.5 Ensemble Learning for the Prediction of Weather Forecasting (EBWF)
An Ensemble Learning technique is a meta-algorithm that combines several ML algorithmic
results into one model of prediction in order to improve the prediction rate. In this research, ensemble
learning is implemented for prediction of the weather forecasting based on Random Forest, Gradient
Boosting Decision Tree, Naive Bayes Bernoulli, and KNN Algorithms. The prediction outcomes of
this evolutionary algorithm are then ensemble using max voting to obtain the best final result. This
bagging concept is implemented by the following algorithm:
f(x)=1
m
m

i=1
fi(x)(16)
Algorithm: 2
Input: big weather dataset
Output: Predictions using classifier
Step 1: Pre-process: the weather input data set is pre-processed using the Section 3.2
Step 2: Feature Selection: the pre-processed input for selecting the relevant features, given in
Section 3.3 using the algorithm 1.
Step 3: Ensemble Methods: selected input features are then given to prediction algorithms such as
Random Forest, Gradient Boosting Decision Tree, Naive Bayes Bernoulli, KNN.
Step 4: Ensemble Learning: Each algorithm outcome is calculated separately. The maximum
prediction result is the final prediction result of the proposed method.
Max (output (RF), output (GBDT), output (NBB), output(KNN))
Step 5: Output: return the prediction result.
4Result and Discussion
4.1 Performance of Metric Measures for Performance Analysis
The proposed work provides the ensemble-based prediction of weather forecasting model using
Random Forest, Gradient Boosting Decision Tree, Naive Bayes Bernoulli, and KNN Algorithm. For
this we are using the following parametric metric measures:

CMC, 2022, vol.73, no.2 3377
Correlation Coefficient (CC)
It reflects the correlation between the attribute of weather forecasting and observation of
attributes.
Corre =cov (A,Obs)
√Var (A)Var (Obs)(17)
Here, cov (A,Obs)is the covariance outcome of weather forecasting model.
Var (A)and Var (Obs)is the variance of the weather forecast model and observation of attributes
in the weather forecasting model.
cov (A,Obs)=
n

i=1Ai−¯
AObsi−Obs(18)
Var (A)=
n

i=1Ai−¯
A2(19)
Var (Obs)=
n

i=1
Obsi−Obs2
(20)
Classification Error Rate or Misclassification (CER)
It is used to calculate as the fraction of predictions were incorrect.
CER =(1−Accuracy)(21)
Agreement Index
This is a measure of the prediction error in the weather forecasting model and it is calculated bas
shown in the following formula:
Index −A=1−n
i=1(Ai−Obsi)2
n
i=1

Ai−obs

+

Obsi−Obs

20≤Index −A≤1 (22)
The range of index agreement (Index −A)varies between 0 and 1; when the Index −Avalue is
close to 1, that refers to an exact matching outcome and 0 refers no agreement at all.
Nash-Sutcliffe Efficiency Coefficient (NSE)
It is used to access the prediction of weather forecasting model based on numerical values and it
can be calculated by:
NSE =1−n
i=1(Ai−Obsi)2
n
i=1ObsiObs2(23)
NSE ranges from −∞ to 1. If the outcome of NSE is 1, that means it matches the observed
attribute perfectly.
Prediction Time (PT)
It calculates the time required to predict the weather forecasting and it is calculated by using:
PT =n∗time (EBWF)(24)

3378 CMC, 2022, vol.73, no.2
Here, time (EBWF)is the time taken for the prediction of weather forecasting algorithms like
Random Forest, Gradient Boosting Decision Tree, Naive Bayes Bernoulli, and KNN Algorithms. time
is evaluated in terms of ms.Andnis the total number of big weather data in the dataset.
Multi-Class Confusion Matrix is used to represent the performance of a multi-class classification
model in terms of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives
(FN).
Classification Accuracy
Classification accuracy =TP +TN
TP +TN +FP +FN X100 (25)
It is used to calculate as the ratio between number of correct classifications to the total number of
classifications.
Classification Precision
Classification precision =TP
TP +FPX100 (26)
It is the ratio between correctly positive labelled classifier to the total number of all positive labels.
Classification Recall
Classification recall =TP
TP +FN ×100 (27)
F1=2∗precision∗Recall
Precision +Recall (28)
Specificity =TN
TN +FP (29)
Tab. 3 show the comparison of Correlation coefficient (CC), Index Agreement (IA), and Nash-
Sutcliffe Efficiency Coefficient (NSE).
Table 3: Comparison of CC, IA and NSE
Algorithm CC IA NSE
RF 0.891 0.783 0.842
GBDT 0.916 0.894 0.906
NBB 0.919 0.904 0.911
KNN 0.883 0.771 0.832
Proposed ensembled (EBWF) 0.937 0.939 0.914
Tab. 3 shows that the proposed ensemble produced the better result compared with other existing
algorithms. Fig. 5 show the calculation of classification error rate using Eq. (21).
Fig. 5 shows that the proposed ensemble-based algorithm produces minimum error rate of 0.043
in the CER train data. The classification error rate of train data set is random forest got 0.116, gradient
boost decision tree 0.074, NBB 0.061 and KNN got 0.107. In the test data, ensemble-based algorithm
produces minimum error rate of 0.051. The classification error rate of test data set is random forest got

CMC, 2022, vol.73, no.2 3379
0.121, gradient boost decision tree 0.082, NBB 0.066 and KNN got 0.109. Fig. 6 show the prediction
time of various ML algorithms.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
RF GBDT NBB KNN Proposed
Ensembled
(EBWF)
Classification Error Rate
CER in Train Data CER in Test Data
Figure 5: Classification error rate
0
10
20
30
40
50
60
1000 1500 2000 2500 3000 3500 4000 4500 5000
Prediciton Time (ms)
No. of Weather Data
RF GBDT NBB KNN Proposed Ensembled (EBWF)
Figure 6: Prediction time
Fig. 6 shows that the prediction time of the bigdata of weather input is analysed. There are various
sizes of the data in the weather dataset. Our proposed EBWF technique requires less time to predict
the weather forecasting. Tab. 4 shows the accuracy rate of both training (80%) and testing (20%) data
in the weather dataset.
Table 4: Accuracy rate in various algorithms
Algorithm Accuracy in train data Accuracy in test data
RF 0.884 0.879
GBDT 0.926 0.918
NBB 0.939 0.934
KNN 0.893 0.891
Proposed ensembled (EBWF) 0.957 0.949
In the Tab. 4, the results of various algorithms are compared based on Accuracy parametric
measures in both training and testing dataset. The proposed ensembled based (EBWF) got 0.957
accuracy rate in the training data set and similarly for the testing dataset 0.949 accuracy rate. It gives

3380 CMC, 2022, vol.73, no.2
better accuracy rate when comparing it with various algorithms. Tab. 5 shows the precision and recall
rate of various algorithms.
Table 5: Precision, recall, specificity
Algorithm Precision Recall Specificity
RF 0.7867 0.6891 0.6734
GBDT 0.8911 0.9011 0.8923
NBB 0.9393 0.9347 0.9211
KNN 0.6156 0.6421 0.6377
Proposed ensembled
(EBWF)
0.9578 0.9492 0.9366
In Tab. 5, the results of various algorithms are compared based on precision and recall parametric
measures. The proposed ensembled based (EBWF) model got 0.9681 precision, 0.9592 recall, and
0.9366 specificity. Fig. 7 shows the F1-score metric rate of various algorithms.
0
0.2
0.4
0.6
0.8
1
1.2
RF GBDT NBB KNN Proposed
Ensembled
(EBWF)
F1-Score
Techniques
Figure 7: F1-Score
Fig. 7 shows the comparison of F1-Score with various algorithms. The results show that our
proposed work outperforms other methods. The performance of the proposed ensemble model with
the proposed EBWF framework shows is better in terms of high accuracy, less error, and less prediction
time. In this research, ensemble learning is implemented for prediction of the weather forecasting based
on Random Forest, Gradient Boosting Decision Tree, Naive Bayes Bernoulli, and KNN Algorithms.
Each algorithm outcome is calculated separately.
Max (output (RF), output (GBDT), output (NBB), output(KNN))
The prediction outcomes of this evolutionary algorithm are then ensemble using max voting to
obtain the best final result. Fig. 8 shows that classifier performance of confusion matrix for the test
data set with classes of {Cloudy,Rainy,Sun shine,Sun rise}with factors of confusion terms are TP,
TN, FP, FN.
In the Fig. 8 describes that confusion matrix of testing data in the data set with the classifiers of
cloudy, rainy, sun shine and sun rise for the ensemble based proposed work.

CMC, 2022, vol.73, no.2 3381
Figure 8: Confusion matrix of test data PWFM
5Conclusion
In this proposed work, ensemble model is compared with existing machine learning algorithms
for predicting weather using essential performance measures. We have observed that ensemble based
EBWF prediction system gives the best weather prediction results. The aim of this proposed work is to
have high accuracy rate, low error, and less prediction time. For the evaluation purpose, we used airport
weather data gathered from different stations in India. This research helps various communities like
fishing, transport, farming etc. In fishing it alert population regarding weather condition. In farming it
helps to plan plantations and irrigation. The results of the proposed EBWF model outperform other
techniques which are Random Forest, KNN, GBDT, and NBB. The ensemble-based EBWF model
resulted with 0.957 accuracy rates using the training data set and similarly 0.949 accuracy rate for
the testing dataset. In our future work, research will explore by connecting IoT devices for collecting
weather data which can help produce high accuracy in less perdition time and improve the decision-
making process.
Acknowledgement: The authors would like to thank the Deanship of Scientific Research at Umm
Al-Qura University for supporting this work by Grant Code: (22UQU4310373DSR10).
Funding Statement: The authors extend their appreciation to the Deanship of Scientific Research at
King Khalid University for funding this work under grant number (RGP 2/42/43). Princess Nourah
bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R135), Princess
Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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