Data Mining Approaches to Predict the Factors that Affect the Agriculture Growth using Stochastic Model
Keywords:
Data Mining, Agriculture productions, Rainfall, Groundwater, Temperature and Stochastic modelAbstract
In the recent times, there has been an increasing demand for efficient strategies in the data mining in agriculture prediction. Data mining is equipment to predict effectively by stochastic model sensing concept. This paper proposes an efficient factor that affects the agriculture growth using different data like rainfall, groundwater and temperature by adopting stochastic modeling and data mining approaches. Firstly, the novel model is proposed to predict the factors affecting the growth of agriculture using stochastic model and numerical illustrations are done and the various expected estimation the sternness of the proposed approach.
References
[1] Rajesh, P. and Karthikeyan, M., “A comparative study of data mining algorithms for decision tree approaches using WEKA tool”, Advances in Natural and Applied Sciences, vol. 11(9), 2017, pp. 230-243.
[2] https://en.wikipedia.org/wiki/Economy_of_India.
[3] https://www.quora.com/An-example-of-stochastic-model
[4] Yan, S., Yu, S., Wu, Y., Pan, D., Dong, J., “Understanding groundwater table using a statistical model”, Water Science and Engineering, vol. 11(1), 2018, pp. 1-7.
[5] Rajesh, P. and M. Karthikeyan, “Prediction of Agriculture Growth and Level of Concentration in Paddy - A Stochastic Data Mining Approach”, Advances in Intelligent Systems and Computing, 2018, pp. 127-139.
[6] EI-Sayed Omran. E., “A stochastic simulation model to early predict susceptible areas to water table level fluctuations in North Sinai, Egypt”, The Egyptian Journal of Remote Sensing and Space Science, vol. 19(2), 2016, pp. 235-257.
[7] Rajesh, P. and Karthikeyan, M., “Predication of Labour Demand in Agriculture Based On Comparative Study of Different Data Using Data Mining and Stochastic Approach”, International Journal of Engineering Science Invention, vol. 2, 2018, pp. 86-89.
[8] Bartholomew, D. J., “The Stochastic model for social processes”,. 3nd ed., John Wiley and Sons, New York, 1982.
[9] Mucherino, A., Papajorgji, P.J., and Pardalos, P.M, “Data mining in agriculture”, Springer Science & Business Media., 2009.
[10] Adhikary, SK., Mahidur Rahman, Md., and Gupta, AD., “A Stochastic Modelling Technique for Predicting Groundwater Table Fluctuations with Time Series Analysis”, International Journal of Applied Sciences and Engineering Research, vol. 1(2), 2012, pp. 238-249.
[11] Mohammad Mirzavand, Seyed Javad Sadatinejad, Hoda Ghasemieh, Rasool Imani and Mehdi Soleymani Motlagh, “Prediction of Ground Water Level in Arid Environment Using a Non-Deterministic Model”, Journal of Water Resource and Protection, vol. 6, 2014, pp. 669-676.
[12] Korn, G. A. and Korn, T. M., “Mathematical Handbook for Scientists and Engineers”, 2nd ed., McGraw-Hill Companies, 2016.
[13] https://en.wikipedia.org/wiki/Laplace_transform.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
