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Estimating Evaporation using Machine Learning Based Ensemble Technique

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Accurately estimating evaporation is necessary for calculating and scheduling irrigation water requirements. Current literature points to the use of individual machine learning models for better estimation of evaporation. However, such methods have not been used in the Indian framework. Moreover, given the diversity of climate, it is necessary to develop an ensemble technique incorporating a significant number of machine learning algorithms to have a better estimation of weekly evaporation. The purpose of this paper is to develop an ensemble technique that makes the machine learning models that have a better estimation of weekly evaporation. The results showed that the Bagging Random Forest model has a much better performance in estimating weekly evaporation compared to other fitted ensemble models. R. S. Parmar | G. B. Chaudhari | S. H. Bhojani "Estimating Evaporation using Machine Learning Based Ensemble Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59847.pdf Paper Url:https://www.ijtsrd.com/engineering/agricultural-engineering/59847/estimating-evaporation-using-machine-learning-based-ensemble-technique/r-s-parmar

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International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 7 Issue 4, July-August 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 985
Estimating Evaporation using Machine
Learning Based Ensemble Technique
R. S. Parmar1, G. B. Chaudhari2, S. H. Bhojani3
1
Professor and Head, College of Agricultural Information Technology, AAU, Anand, Gujarat, India
2
Assistant Professor, College of Agricultural Information Technology, AAU, Anand, Gujarat, India
3
Assistant Professor, IT Center, AAU, Anand, Gujarat, India
ABSTRACT
Accurately estimating evaporation is necessary for calculating and
scheduling irrigation water requirements. Current literature points to
the use of individual machine learning models for better estimation of
evaporation. However, such methods have not been used in the
Indian framework. Moreover, given the diversity of climate, it is
necessary to develop an ensemble technique incorporating a
significant number of machine learning algorithms to have a better
estimation of weekly evaporation. The purpose of this paper is to
develop an ensemble technique that makes the machine learning
models that have a better estimation of weekly evaporation. The
results showed that the Bagging Random Forest model has a much
better performance in estimating weekly evaporation compared to
other fitted ensemble models.
KEYWORDS: Machine Learning, Ensemble, Bagging, Evaporation,
Random Forest
How to cite this paper: R. S. Parmar | G.
B. Chaudhari | S. H. Bhojani
"Estimating Evaporation using Machine
Learning Based Ensemble Technique"
Published in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN:
2456-6470,
Volume-7 | Issue-4,
August 2023, pp.985-990, URL:
www.ijtsrd.com/papers/ijtsrd59847.pdf
Copyright © 2023 by author (s) and
International Journal of Trend in
Scientific Research and Development
Journal. This is an
Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
INTRODUCTION
Estimating evaporation is essential for managing
water resources, optimizing irrigation schedules, and
modeling agricultural production (Konapala et al.,
2020; Short Gianotti et al., 2020). Besides,
evaporation has significant importance in studying
climate change because this parameter scatters a good
proportion of the global precipitation (Ma, 2018;
Wang et al. 2019). Few studies have been conducted
to solve different water resource problems using
different artificial intelligence approaches namely
random forest, support vector machine, extreme
learning machine, feed-forward neural network,
Gaussian process regression, and, gradient boosting
model (Hameed et al.,2021; Ghorbani et al.,2020;
Ashrafzadeh et al.,2018). Ensemble methods are
techniques that aim at improving the accuracy of
results in models by combining multiple models
instead of using a single model. They combine
multiple algorithms to produce better classification
and regression performance. Ensemble techniques
improve the accuracy of fitted models by combining
multiple models in place of using a single model.
Bagging is an ensemble technique that helps to
improve the performance and accuracy of machine
learning models. It is used to reduce the variance of
an estimation model. Bagging avoids the overfitting
of data and is widely used for regression models.
Ensemble techniques have been used to develop a
system of crop yield estimation and fertilizer
recommendation (Kumaravel et al., 2020). Machine
learning-based classification and regression
techniques are widely used in agriculture for
estimating outcomes using datasets that can often
comprise hundreds of features and observations
(Kamani et al. 2019, Kamani et al. 2021, Parmar et
al. 2022). Estimation of wheat crop yield using the
machine learning techniques and the use of different
activation functions have been used and
recommended to choose the activation function
consideting the research purpose and they type of
datasets (Shital et al., 2021). The purpose of this
paper is to determine the effectiveness of the machine
IJTSRD59847
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 986
learning-based ensemble technique for estimating
weekly evaporation using weekly weather data of the
Anand district of Gujarat.
OBJECTIVE
To develop an ensemble technique that makes the
machine learning models for better estimation of
weekly evaporation.
MATERIALS AND METHODS
The present study was undertaken to explore the
possibility of estimating evaporation by using the
effect of weekly weather variables. For judging the
joint influence of weather variables, a week-wise
approach was considered. The weekly weather data
for Anand viz; temperature, sunshine hour, wind
velocity, relative humidity, and evaporation for 43
years i.e. from 1980-2022 were collected from the
Department of Agricultural Meteorology, Anand
Agricultural University, Anand.
The ensemble is one of the most popular and
successful techniques in machine learning. Ensemble
learning is a learning method that consists of
combining multiple machine learning models. A
problem in machine learning is that individual models
tend to perform poorly. The individual models are
known as weak learners. Weak learners either have a
high bias or high variance. Ensemble learning
improves a model’s performance in mainly three
ways:
1. By reducing the variance of weak learners
2. By reducing the bias of weak learners,
3. By improving the overall accuracy of strong
learners.
Bagging (Bootstrap aggregating) is used to reduce the
variance of weak learners. Fig.1 depicts a conceptual
view of the Bagging (Bootstrap aggregating) Process.
Fig. 1. Conceptual view of Bagging (Bootstrap aggregating) Process
RESULTS AND DISCUSSION
An open source weka version 3.8.5 is an ensemble toolkit for data regression and visualization. It was used to
evaluate the performance and effectiveness of machine learning-enabled ensemble evaporation estimation
models and 6 ensemble models were built from the bagging technique.
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 987
Fig. 2. Selected Weekly Weather Variables Distribution
The Bagging Linear Regression, Bagging Neural Network, Bagging REP Tree, Bagging Random Forest,
Bagging KNN, and Bagging Support Vector Machines models were used to examine estimating evaporation.
The result of each fitted ensemble model is checked in terms of R2
, MAE, RMSE, RAE, and RRSE. Fig. 2
demonstrates that the selected weekly weather variables have different distribution ranges.
Table 1 shows the characteristics of fitted machine learning-based ensemble models. Out of 6 formations of
ensemble models in this research work Bagging Random Forest has achieved better performance than other
fitted models. In general, it could be observed that Bagging Random Forest is the best-fitted ensemble model to
estimate evaporation.
Table 1. Characteristics of Fitted Machine Learning based Ensemble Models
Ensemble
Models
Parameters
Mean
Absolute
Error (MAE)
Root Mean
Squared Error
(RMSE)
Relative
Absolute
Error (RAE)
Root Relative
Squared Error
(RRSE)
Coefficient of
Determination
(R2)
Bagging Linear
Regression
0.6419 0.8202 33.41 % 35.65 % 87.27 %
Bagging Neural
Network
0.5475 0.7176 28.50 % 31.19 % 90.27 %
Bagging REP
Tree
0.5604 0.7362 29.17 % 32.00 % 89.76 %
Bagging Random
Forest
0.5345 0.7010 27.82 % 30.47 % 90.71 %
Bagging KNN 0.6406 0.8565 33.34 % 37.23 % 86.30 %
Bagging Support
Vector Machines
0.6413 0.8213 33.38 % 35.70 % 87.25 %
Fig. 3 demonstrates the estimation accuracy of different fitted ensemble models. Bagging Random Forest has
better estimation accuracy than other fitted ensemble models with 90.70 %, followed by Neural Network with
90.30 %. Bagging KNN has the lowest estimations accuracy with 86.30 %.
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 988
Fig. 3. Estimation Accuracy of Different Fitted Ensemble Models
Fig. 4 shows the error results of the different fitted ensemble models. Bagging Random Forest has the lowest
Mean Absolute Error (MAE) of 0.53 and Root Mean Squared Error (RMSE) of 0.70. This reveals minimal error
reported during the estimation of evaporation. Bagging KNN has the highest error with 0.64 and 0.86 of MAE
and RMSE, respectively.
Fig. 4. Error Results of Different Fitted Ensemble Models
Fig. 5 depicts the Mean Absolute Error (MAE) of the Bagging Random Forest model. The measure of estimation
accuracy is also called MAE and a low MAE suggests the fitted ensemble model is good at an estimation of
evaporation. Multiple Correlation Coefficient (R) is a measure of how well evaporation can be estimated using a
linear function of a set of weekly weather variables. Usually, a higher R-value indicates a better estimation of the
evaporation from the selected weekly weather variables. MAE (0.53) and R (0.95) values were low and high
respectively and thus indicated an excellent job by the fitted ensemble model.
Fig. 5. Mean Absolute Error of Bagging Random Forest
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 989
The estimated evaporation is revealed in Fig. 6. It is observed that the actual evaporation and the estimated
evaporation are very close to each other. The estimated evaporation showed deviations from actual evaporation
ranging between –1.7 to 2.2.
Fig. 6. Comparisons between Actual and Simulated estimation of evaporation using Bagging Random
Forest
CONCLUSION
Bagging is an ensemble machine learning technique
that helps to avoid overfitting data. It is a model
averaging procedure that is often used with decision
trees but can also be applied to other algorithms. It
was observed that bagging random forest was the best
fitted ensemble model for estimating weekly
evaporation by achieving the highest coefficient of
determination (R2
) of 90.71 % as compared with
other fitted models. The fitted ensemble model has
the lowest MAE of 0.53 and RMSE of 0.95. There is
also a significant scope for using the ensemble ML
technique in estimating the weekly evaporation of
other data samples and extending the support of
analysis. Hence, it can be concluded that the study
helps the researcher in efficient ensemble model
selection for estimating weekly evaporation.
Scientific Community has recommended using the
ensemble technique bagging for estimating weekly
evaporation.
REFERENCES
[1] Ashrafzadeh, A., Malik, A., Jothiprakash, V.,
Ghorbani, M. A. and Biazar, S.M. (2018).
“Estimation of daily pan evaporation using
neural networks and meta-heuristic
approaches” ISH Journal of Hydraulic
Engineering, 26(4): 421–429.
[2] Ghorbani, M.A., Deo, R.C., Kim, Hasanpour
Kashani, S.M., Karimi, V. and Izadkhah, M.
(2020). “Development and evaluation of the
cascade correlation neural network and the
random forest models for river stage and river
flow prediction in Australia,” Soft Computing,
24(16): 12079–12090.
[3] Hameed, M.M., Alomar, M.K. and Mohd
Razali, S.F. (2021). “Application of artificial
intelligence models for evapotranspiration
prediction along the southern coast of Turkey”
Complexity, 2021 Article ID 8850243, 20
pages.
[4] Kamani, G.J., Parmar, R.S., Ghodasara, Y.R.
(2019). Data normalization in data mining
using graphical user interface: a pre-processing
stage, Guj. J. Ext. Edu, 30(2): 106-109.
[5] Kamani, G.J., Parmar, R.S., Ghodasara, Y.R.
(2021). Machine learning models for
productivity trend of Rice crop, Guj. J. Ext.
Edu., 32(2): 189-196.
[6] Konapala, G., Mishra, A. K. Wada, Y. and
Mann, M.E. (2020). “Climate change will
affect global water availability through
compounding changes in seasonal precipitation
and evaporation,” Nature Communications,
11(3044): 1–10.
[7] Kumaravel, A. and Saranya, K.G. (2020). Crop
Yield Prediction, Forecasting and Fertilizer
Recommendation using Voting Based
Ensemble Classifier. Seventh Sense Research
Group (SSRG) International Journal of
Computer Science and Engineering, 7(5): 1-4.
[8] Ma, W. (2018). “Research progresses of flash
evaporation in aerospace applications”.
International Journal of Aerospace
Engineering, 2018. Article ID 3686802.
[9] Parmar, R.S., Vegad, N.M., Mehra, V.I. (2022).
An attitude of PG scholars of agricultural
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 990
extension towards Application of mobile
technology using artificial intelligence
technique, Guj. J. Ext. Edu, 33(1): 136-143.
[10] Parmar, R.S., Mehra, V.I., Kamani, G.J. (2022).
Analyzing Soil fertility using Data Mining
Techniques, Guj. J. Ext. Edu, 34(2): 47-50.
[11] Short Gianotti, D.J., Akbar, R., Feldman, A.F.,
Salvucci, G.D. and Entekhabi, D. (2020).
“Terrestrial evaporation and moisture drainage
in a warmer climate,” Geophysical Research
Letters, 47(5), Article ID e2019GL086498.
[12] Wang, S., Lian, J., Peng, Y., Hu, B. and Chen,
H. (2019). “Generalized reference
evapotranspiration models with limited climatic
data based on random forest and gene
expression programming in Guangxi, China,”
Agricultural Water Management, 221: 220–
230.
[13] Bhojani, S. H., and Bhatt, N. (2021).
“Performance Analysis of Activation Functions
for Wheat Crop Yield Prediction.” In IOP
Conference Series: Materials Science and
Engineering (Vol. 1042, No. 1, p. 012015). IOP
Publishing.

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Estimating Evaporation using Machine Learning Based Ensemble Technique

  • 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 7 Issue 4, July-August 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 985 Estimating Evaporation using Machine Learning Based Ensemble Technique R. S. Parmar1, G. B. Chaudhari2, S. H. Bhojani3 1 Professor and Head, College of Agricultural Information Technology, AAU, Anand, Gujarat, India 2 Assistant Professor, College of Agricultural Information Technology, AAU, Anand, Gujarat, India 3 Assistant Professor, IT Center, AAU, Anand, Gujarat, India ABSTRACT Accurately estimating evaporation is necessary for calculating and scheduling irrigation water requirements. Current literature points to the use of individual machine learning models for better estimation of evaporation. However, such methods have not been used in the Indian framework. Moreover, given the diversity of climate, it is necessary to develop an ensemble technique incorporating a significant number of machine learning algorithms to have a better estimation of weekly evaporation. The purpose of this paper is to develop an ensemble technique that makes the machine learning models that have a better estimation of weekly evaporation. The results showed that the Bagging Random Forest model has a much better performance in estimating weekly evaporation compared to other fitted ensemble models. KEYWORDS: Machine Learning, Ensemble, Bagging, Evaporation, Random Forest How to cite this paper: R. S. Parmar | G. B. Chaudhari | S. H. Bhojani "Estimating Evaporation using Machine Learning Based Ensemble Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, pp.985-990, URL: www.ijtsrd.com/papers/ijtsrd59847.pdf Copyright © 2023 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0) INTRODUCTION Estimating evaporation is essential for managing water resources, optimizing irrigation schedules, and modeling agricultural production (Konapala et al., 2020; Short Gianotti et al., 2020). Besides, evaporation has significant importance in studying climate change because this parameter scatters a good proportion of the global precipitation (Ma, 2018; Wang et al. 2019). Few studies have been conducted to solve different water resource problems using different artificial intelligence approaches namely random forest, support vector machine, extreme learning machine, feed-forward neural network, Gaussian process regression, and, gradient boosting model (Hameed et al.,2021; Ghorbani et al.,2020; Ashrafzadeh et al.,2018). Ensemble methods are techniques that aim at improving the accuracy of results in models by combining multiple models instead of using a single model. They combine multiple algorithms to produce better classification and regression performance. Ensemble techniques improve the accuracy of fitted models by combining multiple models in place of using a single model. Bagging is an ensemble technique that helps to improve the performance and accuracy of machine learning models. It is used to reduce the variance of an estimation model. Bagging avoids the overfitting of data and is widely used for regression models. Ensemble techniques have been used to develop a system of crop yield estimation and fertilizer recommendation (Kumaravel et al., 2020). Machine learning-based classification and regression techniques are widely used in agriculture for estimating outcomes using datasets that can often comprise hundreds of features and observations (Kamani et al. 2019, Kamani et al. 2021, Parmar et al. 2022). Estimation of wheat crop yield using the machine learning techniques and the use of different activation functions have been used and recommended to choose the activation function consideting the research purpose and they type of datasets (Shital et al., 2021). The purpose of this paper is to determine the effectiveness of the machine IJTSRD59847
  • 2. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 986 learning-based ensemble technique for estimating weekly evaporation using weekly weather data of the Anand district of Gujarat. OBJECTIVE To develop an ensemble technique that makes the machine learning models for better estimation of weekly evaporation. MATERIALS AND METHODS The present study was undertaken to explore the possibility of estimating evaporation by using the effect of weekly weather variables. For judging the joint influence of weather variables, a week-wise approach was considered. The weekly weather data for Anand viz; temperature, sunshine hour, wind velocity, relative humidity, and evaporation for 43 years i.e. from 1980-2022 were collected from the Department of Agricultural Meteorology, Anand Agricultural University, Anand. The ensemble is one of the most popular and successful techniques in machine learning. Ensemble learning is a learning method that consists of combining multiple machine learning models. A problem in machine learning is that individual models tend to perform poorly. The individual models are known as weak learners. Weak learners either have a high bias or high variance. Ensemble learning improves a model’s performance in mainly three ways: 1. By reducing the variance of weak learners 2. By reducing the bias of weak learners, 3. By improving the overall accuracy of strong learners. Bagging (Bootstrap aggregating) is used to reduce the variance of weak learners. Fig.1 depicts a conceptual view of the Bagging (Bootstrap aggregating) Process. Fig. 1. Conceptual view of Bagging (Bootstrap aggregating) Process RESULTS AND DISCUSSION An open source weka version 3.8.5 is an ensemble toolkit for data regression and visualization. It was used to evaluate the performance and effectiveness of machine learning-enabled ensemble evaporation estimation models and 6 ensemble models were built from the bagging technique.
  • 3. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 987 Fig. 2. Selected Weekly Weather Variables Distribution The Bagging Linear Regression, Bagging Neural Network, Bagging REP Tree, Bagging Random Forest, Bagging KNN, and Bagging Support Vector Machines models were used to examine estimating evaporation. The result of each fitted ensemble model is checked in terms of R2 , MAE, RMSE, RAE, and RRSE. Fig. 2 demonstrates that the selected weekly weather variables have different distribution ranges. Table 1 shows the characteristics of fitted machine learning-based ensemble models. Out of 6 formations of ensemble models in this research work Bagging Random Forest has achieved better performance than other fitted models. In general, it could be observed that Bagging Random Forest is the best-fitted ensemble model to estimate evaporation. Table 1. Characteristics of Fitted Machine Learning based Ensemble Models Ensemble Models Parameters Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Relative Absolute Error (RAE) Root Relative Squared Error (RRSE) Coefficient of Determination (R2) Bagging Linear Regression 0.6419 0.8202 33.41 % 35.65 % 87.27 % Bagging Neural Network 0.5475 0.7176 28.50 % 31.19 % 90.27 % Bagging REP Tree 0.5604 0.7362 29.17 % 32.00 % 89.76 % Bagging Random Forest 0.5345 0.7010 27.82 % 30.47 % 90.71 % Bagging KNN 0.6406 0.8565 33.34 % 37.23 % 86.30 % Bagging Support Vector Machines 0.6413 0.8213 33.38 % 35.70 % 87.25 % Fig. 3 demonstrates the estimation accuracy of different fitted ensemble models. Bagging Random Forest has better estimation accuracy than other fitted ensemble models with 90.70 %, followed by Neural Network with 90.30 %. Bagging KNN has the lowest estimations accuracy with 86.30 %.
  • 4. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 988 Fig. 3. Estimation Accuracy of Different Fitted Ensemble Models Fig. 4 shows the error results of the different fitted ensemble models. Bagging Random Forest has the lowest Mean Absolute Error (MAE) of 0.53 and Root Mean Squared Error (RMSE) of 0.70. This reveals minimal error reported during the estimation of evaporation. Bagging KNN has the highest error with 0.64 and 0.86 of MAE and RMSE, respectively. Fig. 4. Error Results of Different Fitted Ensemble Models Fig. 5 depicts the Mean Absolute Error (MAE) of the Bagging Random Forest model. The measure of estimation accuracy is also called MAE and a low MAE suggests the fitted ensemble model is good at an estimation of evaporation. Multiple Correlation Coefficient (R) is a measure of how well evaporation can be estimated using a linear function of a set of weekly weather variables. Usually, a higher R-value indicates a better estimation of the evaporation from the selected weekly weather variables. MAE (0.53) and R (0.95) values were low and high respectively and thus indicated an excellent job by the fitted ensemble model. Fig. 5. Mean Absolute Error of Bagging Random Forest
  • 5. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 989 The estimated evaporation is revealed in Fig. 6. It is observed that the actual evaporation and the estimated evaporation are very close to each other. The estimated evaporation showed deviations from actual evaporation ranging between –1.7 to 2.2. Fig. 6. Comparisons between Actual and Simulated estimation of evaporation using Bagging Random Forest CONCLUSION Bagging is an ensemble machine learning technique that helps to avoid overfitting data. It is a model averaging procedure that is often used with decision trees but can also be applied to other algorithms. It was observed that bagging random forest was the best fitted ensemble model for estimating weekly evaporation by achieving the highest coefficient of determination (R2 ) of 90.71 % as compared with other fitted models. The fitted ensemble model has the lowest MAE of 0.53 and RMSE of 0.95. There is also a significant scope for using the ensemble ML technique in estimating the weekly evaporation of other data samples and extending the support of analysis. Hence, it can be concluded that the study helps the researcher in efficient ensemble model selection for estimating weekly evaporation. Scientific Community has recommended using the ensemble technique bagging for estimating weekly evaporation. REFERENCES [1] Ashrafzadeh, A., Malik, A., Jothiprakash, V., Ghorbani, M. A. and Biazar, S.M. (2018). “Estimation of daily pan evaporation using neural networks and meta-heuristic approaches” ISH Journal of Hydraulic Engineering, 26(4): 421–429. [2] Ghorbani, M.A., Deo, R.C., Kim, Hasanpour Kashani, S.M., Karimi, V. and Izadkhah, M. (2020). “Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia,” Soft Computing, 24(16): 12079–12090. [3] Hameed, M.M., Alomar, M.K. and Mohd Razali, S.F. (2021). “Application of artificial intelligence models for evapotranspiration prediction along the southern coast of Turkey” Complexity, 2021 Article ID 8850243, 20 pages. [4] Kamani, G.J., Parmar, R.S., Ghodasara, Y.R. (2019). Data normalization in data mining using graphical user interface: a pre-processing stage, Guj. J. Ext. Edu, 30(2): 106-109. [5] Kamani, G.J., Parmar, R.S., Ghodasara, Y.R. (2021). Machine learning models for productivity trend of Rice crop, Guj. J. Ext. Edu., 32(2): 189-196. [6] Konapala, G., Mishra, A. K. Wada, Y. and Mann, M.E. (2020). “Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation,” Nature Communications, 11(3044): 1–10. [7] Kumaravel, A. and Saranya, K.G. (2020). Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier. Seventh Sense Research Group (SSRG) International Journal of Computer Science and Engineering, 7(5): 1-4. [8] Ma, W. (2018). “Research progresses of flash evaporation in aerospace applications”. International Journal of Aerospace Engineering, 2018. Article ID 3686802. [9] Parmar, R.S., Vegad, N.M., Mehra, V.I. (2022). An attitude of PG scholars of agricultural
  • 6. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59847 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 990 extension towards Application of mobile technology using artificial intelligence technique, Guj. J. Ext. Edu, 33(1): 136-143. [10] Parmar, R.S., Mehra, V.I., Kamani, G.J. (2022). Analyzing Soil fertility using Data Mining Techniques, Guj. J. Ext. Edu, 34(2): 47-50. [11] Short Gianotti, D.J., Akbar, R., Feldman, A.F., Salvucci, G.D. and Entekhabi, D. (2020). “Terrestrial evaporation and moisture drainage in a warmer climate,” Geophysical Research Letters, 47(5), Article ID e2019GL086498. [12] Wang, S., Lian, J., Peng, Y., Hu, B. and Chen, H. (2019). “Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China,” Agricultural Water Management, 221: 220– 230. [13] Bhojani, S. H., and Bhatt, N. (2021). “Performance Analysis of Activation Functions for Wheat Crop Yield Prediction.” In IOP Conference Series: Materials Science and Engineering (Vol. 1042, No. 1, p. 012015). IOP Publishing.