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EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK

Year 2020, Volume: 9 Issue: 1, 186 - 194, 30.01.2020
https://doi.org/10.28948/ngumuh.529418

Abstract

Soil plays a vital role in the climate system.
This paper performs a hybrid methodology that consists of particle swarm optimization
(PSO) and artificial neural network (ANN) to estimate soil moisture (SM) by
considering different parameters that include air temperature, time, relative
humidity and soil temperature. Besides, this paper investigates the effects of
the parameters of PSO-ANN by utilizing from the response surface. PSO algorithm
is involved in the process of changing the weights of ANN. The coefficient of
determination and mean absolute error are chosen to measure the performance of
the performed hybrid PSO-ANN. The numerical results show that hybrid PSO-ANN is
applied to estimate SM successfully.

References

  • [1] Shukla G., Garg R. D., Srivastava H. S., Garg P. K., “An effective implementation and assessment of a random forest classifier as a soil spatial predictive model”, International Journal of Remote Sensing, 39(8), 2637-2669, 2018. [2] Qu Y., Qian X., Song H., Xing Y., Li Z., Tan, J., “Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing”, Water, 10, 423, 2018. [3] Moosavi V., Talebi A., Mokhtari M. H., Hadian M. R., “Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches”, International journal of remote sensing, 37(23), 5605-5631, 2016. [4] Kundu D., Vervoort R. W., van Ogtrop F. F., “The value of remotely sensed surface soil moisture for model calibration using SWAT”, Hydrological Processes, 31(15), 2764-2780, 2017. [5] Yang Q., Zuo H., Li W., “Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region”, Plos One, 11(3), 2016. [6] Eberhart R. and Kennedy J., “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium Micro Machine and Human Science, Nagoya, Japan, 39-43, 1995. [7] https://www.utm.utoronto.ca/geography/resources/environmental-datasets, 04.02.2019
Year 2020, Volume: 9 Issue: 1, 186 - 194, 30.01.2020
https://doi.org/10.28948/ngumuh.529418

Abstract

References

  • [1] Shukla G., Garg R. D., Srivastava H. S., Garg P. K., “An effective implementation and assessment of a random forest classifier as a soil spatial predictive model”, International Journal of Remote Sensing, 39(8), 2637-2669, 2018. [2] Qu Y., Qian X., Song H., Xing Y., Li Z., Tan, J., “Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing”, Water, 10, 423, 2018. [3] Moosavi V., Talebi A., Mokhtari M. H., Hadian M. R., “Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches”, International journal of remote sensing, 37(23), 5605-5631, 2016. [4] Kundu D., Vervoort R. W., van Ogtrop F. F., “The value of remotely sensed surface soil moisture for model calibration using SWAT”, Hydrological Processes, 31(15), 2764-2780, 2017. [5] Yang Q., Zuo H., Li W., “Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region”, Plos One, 11(3), 2016. [6] Eberhart R. and Kennedy J., “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium Micro Machine and Human Science, Nagoya, Japan, 39-43, 1995. [7] https://www.utm.utoronto.ca/geography/resources/environmental-datasets, 04.02.2019
There are 1 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Industrial Engineering
Authors

Engin Pekel 0000-0002-5295-8013

Publication Date January 30, 2020
Submission Date February 19, 2019
Acceptance Date December 5, 2019
Published in Issue Year 2020 Volume: 9 Issue: 1

Cite

APA Pekel, E. (2020). EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9(1), 186-194. https://doi.org/10.28948/ngumuh.529418
AMA Pekel E. EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK. NOHU J. Eng. Sci. January 2020;9(1):186-194. doi:10.28948/ngumuh.529418
Chicago Pekel, Engin. “EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 9, no. 1 (January 2020): 186-94. https://doi.org/10.28948/ngumuh.529418.
EndNote Pekel E (January 1, 2020) EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 9 1 186–194.
IEEE E. Pekel, “EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK”, NOHU J. Eng. Sci., vol. 9, no. 1, pp. 186–194, 2020, doi: 10.28948/ngumuh.529418.
ISNAD Pekel, Engin. “EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 9/1 (January 2020), 186-194. https://doi.org/10.28948/ngumuh.529418.
JAMA Pekel E. EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK. NOHU J. Eng. Sci. 2020;9:186–194.
MLA Pekel, Engin. “EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 1, 2020, pp. 186-94, doi:10.28948/ngumuh.529418.
Vancouver Pekel E. EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK. NOHU J. Eng. Sci. 2020;9(1):186-94.

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