Fuzzy Logic Model for the Prediction of Groundwater Level in Amaravathi River Minor Basin

G. R. Umamaheswari; Dr. D. Kalamani

doi:https://doi.org/10.14445/22315373/IJMTT-V11P505

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 11 | Number 1 | Year 2014 | Article Id. IJMTT-V11P505 | DOI : https://doi.org/10.14445/22315373/IJMTT-V11P505

Fuzzy Logic Model for the Prediction of Groundwater Level in Amaravathi River Minor Basin

G. R. Umamaheswari, Dr. D. Kalamani

Citation :

G. R. Umamaheswari, Dr. D. Kalamani, "Fuzzy Logic Model for the Prediction of Groundwater Level in Amaravathi River Minor Basin," International Journal of Mathematics Trends and Technology (IJMTT), vol. 11, no. 1, pp. 46-50, 2014. Crossref, https://doi.org/10.14445/22315373/IJMTT-V11P505

Abstract

Water is a renewable resource and we are utilizing only less than 15% of surplus water from rainfall for domestic, irrigation, industrial and other research purposes. Due increased population, industrial development and fashion of living from the society has always experienced the problem of shortage of water and higher level of groundwater pollution. In order to meet such shortage it is necessary to know the groundwater level at various locations for its availability and sustainability of aquifer system to meet future demand. The prediction of groundwater level is one of the challenging aspects to understand the groundwater resource in order replicate the exact field condition. Groundwater fluctuation level prediction is consists of many practical uncertainties and result with inaccuracy in prediction by many mathematical approaches. Advanced Fuzzy logic is an excellent mathematical tool to handle such uncertainty issues. In this paper, an approach were made by using Adaptive Neuro Fuzzy Inference System (ANFIS) to predict the groundwater level fluctuation in Amaravathi river minor basin.

Keywords

Groundwater, Amaravathi River Minor Basin, ANFIS, Sustainability, Fluctuation

References

[1] D. C. S. Bisht, M. Mohan Raju,, M. C. Joshi, simulation of water table elevation fluctuation using fuzzy-logic and anfis, Computer Modelling and New Technologies, 2009, Vol.13, No.2, 16–23.
[2] Bahat, M., Inbar, G., Schneider, M. A Fuzzy Irrigation Controller System, Artificial Intelligence, Vol. 139, 2000, pp. 137–145.
[3] Bardossy, A., Disse, M. Fuzzy Rule Based Models for Infiltration, Water Resources Research, Vol. 29, No 2, 1993, pp. 373–382.
[4] Bardossy, A., Duckstein, L. Analysis of Karstic Aquifer Management Problem by Fuzzy Composite Programming, Water Resources. Bulletin, Vol. 28, No 1, 1992, pp. 63–73.
[5] Coppola, Duckstein, L., Dvis, D. Fuzzy Rule Based Methodology for Estimating Monthly Ground Water Recharge in a Temperate Watershed, Journal of Hydrologic Engineering, Vol. 7, No 4, 2000, pp. 326–335.
[6] Fontane, D. G., Gates, T. K., Moncada, E. Planning Reservoir Operations with Imprecise Objectives, Journal of Water Resources Planning and Management, Vol. 123, No 3, 1997, pp. 154–168.
[7] Lohani, A. K., Goel, N. K., Bhatia, K .K. S., Takagi-Sugeno. Fuzzy Inference System for Modelling Stage-Discharge Relationship, Journal of Hydrology, Vol. 331, 2006, pp. 146–160.
[8] Lohani, A. K., Goel, N. K., Bhatia, K. K. S. Deriving Stage Discharge Sediment Concentration Relationships Using Fuzzy Logic, Hydrological Sciences – Journal-des Sciences Hydrologiques, Vol. 52, No 4, 2007, pp. 793–807.
[9] Panigrahi, D. P., Mujumdar, P. P. Reservoir Operation Modelling with Fuzzy Logic, Water ResourcesManagement, Vol. 123, No 3, 2000, pp. 154–168.
[10] Rajasekaran, S., Pai Vijayalakshmi, G. A. Neural Networks. Fuzzy Logic and Genetic Algorithms. New Delhi: Prentice Hall of India Pvt. Ltd., 2007.
[11] Zadeh, L. A. Fuzzy sets, Information and Control, Vol. 8, No 3, 1965, pp. 338–353.