GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING

Authors

  • Dr. J Rajaram
  • D Navya
  • E Krishna
  • G Harika

Keywords:

Soil Moisture, SVM, ANN, Machine Learning

Abstract

This Paper introduces the implementation of different supervised learning techniques for producing accurate estimates of ground water, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited.. The new algorithm enhances the temporal resolution of high spatial resolution of soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research.

References

Y. H. Kerr, P. Waldteufel, J.-P. Wigneron, J. Martinuzzi, J. Font, and M. Berger, “Soil moisture retrieval from space: The soil moisture and ocean salinity (smos) mission,” IEEE transactions on Geoscience and remote sensing, vol. 39, no. 8, pp. 1729–1735, 2001.

A. L. Kaleita, L. Tian, and H. Yao, “Soil moisture estimation from remotely sensed data,” in American Society of Agricultural Engineers Annual International meeting, Las Vegas, NV, July 27-30, 2003.

J. F. Power, P. L. Brown, T. J. Army, and M. G. Klages, “Phosphorus responses by dryland spring wheat as influenced by moisture supplies,” Agronomy Journal, vol. 53, pp. 106–108, 1961.

S. Machado, E. D. Bynum, T. L. Archer, R. J. Lascano, L. T. Wilson, L. Borodvsky, E. Scarra, D.

M. Nesmith, and W. Xu, “Spatial and temporal variability of corn grain yield: site specific relationships to biotic and abiotic factors,” Precision Agriculture, vol. 2, pp. 359–376, 2000.

S. Gorthi and H. Dou, “Prediction models for estimation of soil moisture content,” in 2011 ASME

/IEEE International Conference on Mechatronic and Embedded Systems and Applications (MESA2011), Washington, DC, August 28-31, 2011.

M. G. Genton, “Classes of kernels for machine learning: A statistics perspective,” Journal of Machine Learning, vol. 2, pp. 299–312, 2001.

M. E. Tipping, “Sparse bayesian learning and the relevance vector machines,” Journal of Machine Learning, vol. 1, pp. 211–244, 2001.

R. H. Reichle, R. D. Koster, J. Dong, and A. A. Berg, “Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation,” Journal of Hydrometeorology, vol. 5, no. 3, pp. 430–442, 2004.

Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology,407(1–4), 28–40.

Alizamir, M., Kisi, O., & Zounemat-Kermani, M. (2017). Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological Sciences

Journal,63(1), 63–73.

Ehteram, M., Singh, V. P., Ferdowsi, A., Mousavi, S. F., Farzin, S., Karami, H., et al. (2019b). An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration. PLoS ONE,14(5),

e0217499.

Banerjee, P., et al. (2009), Forecasting of groundwater level in hard rock region using artificial neural network, Environmental Geology, 58(6),n1239-1246.

Yoon, H., et al. (2011), A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, Journal of Hydrology, 396(1-2), 128- 138.

Shirmohammadi, B.; Vafakhah, M.; Moosavi, V.; Moghaddamnia, A. Application of Several Data- Driven Techniques for Predicting Groundwater Level. Water Resour. Manag. 2013, 27, 419–432.

Rahmati, O.; Pourghasemi, H.R.; Melesse, A.M. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. CATENA 2016, 137, 360–372.

Sahoo, S.; Russo, T.A.; Elliott, J.; Foster, I. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resour. Res. 2017, 53, 3878–3895.

Downloads

Published

2020-12-30

How to Cite

Rajaram, D. J. . ., Navya, D. . ., Krishna, E. . ., & Harika, G. . . (2020). GROUND WATER LEVEL PREDICTION USING MACHINE LEARNING. The Journal of Contemporary Issues in Business and Government, 26(3), 106–112. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/525

Issue

Vol. 26 No. 3 (2020): The journal of contemporary issues in business and government

Section

Articles

License

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

Most read articles by the same author(s)