Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India

Modeling Earth Systems and Environment

Volumn 2, Issue 4, 2016, Pages 1-10

  Wagh, Vasant Madhav ^a   Panaskar, Dipak Baburao ^a   Muley, Aniket Avinash ^a   Mukate, Shrikant Vitthal ^a   Lolage, Yogesh Popatrao ^a   Aamalawar, Manesh Laxman ^a  

^a SWAMI RAMANAND TEERTH MARATHWADA UNIVERSITY   (India)

Author keywords

Backpropagation algorithm; Irrigation suitability; Nanded tehsil; Neural network

Indexed keywords

ADSORPTION; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; GROUNDWATER; HYDROLOGICAL MODELING; IRRIGATION; PREDICTION; SODIUM;

INDIA; MAHARASHTRA;

EID: 85036604842     PISSN: 23636203     EISSN: 23636211     Source Type: Journal    
DOI: 10.1007/s40808-016-0250-3     Document Type: Article

References (22)

1. APHA (2005) Standard methods for the examination of water and wastewater, 21st edn. American Public Health Association, New York
2. Ayers RS, Westcot DW (1994) Water quality for agriculture. Rome: FAO. Irrigation and drainage paper, vol 29, p 174
3. Central Ground Water Board (CGWB) (2010) Groundwater quality in shallow aquifers of India. Ministry of Water Resources, Govt. of India, Nagpur, p 294
4. Central Ground Water Board (CGWB) (2013) Groundwater information Nanded district Maharashtra. Ministry of Water Resources, Govt. of India, Nagpur, p 294
5. Chang FJ, Tsai WP, Chen HK, Yam RSW, Herricks EE (2013) A self-organizing radial basis network for estimating riverine fish diversity. J Hydrol 476:280–289 DOI: 10.1016/j.jhydrol.2012.10.038
6. Doneen LD (1964) Water quality for agriculture. Department of Irrigation, University of Calfornia, Calfornia
7. Eaton FM (1950) Significance of carbonates in irrigation waters. Soil Sci 69(2):123–134 DOI: 10.1097/00010694-195002000-00004
8. Fijani E, Nadiri AA, Moghaddam AA, Tsai FTC, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran. J Hydrol 503:89–100 DOI: 10.1016/j.jhydrol.2013.08.038
9. Ghavidel SZZ, Montaseri M (2014) Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin. Stoch Env Res Risk Assess 28(8):2101–2118 DOI: 10.1007/s00477-014-0899-y
10. Goyal SK, Chaudhary BS, Singh O, Sethi GK, Thakur PK (2010) GIS based spatial distribution mapping and suitability evaluation of groundwater quality for domestic and agricultural purpose in Kaithal district, Haryana state, India. Environ Earth Sci 61(8):1587–1597 DOI: 10.1007/s12665-010-0472-z
11. Jangam CM, Sanam SR, Chaturvedi MK, Padmakar C, Pujari PR, Labhasetwar PK (2015) Impact assessment of on-site sanitation system on groundwater quality in alluvial settings: a case study from Lucknow city in North India. Environ Monit Assess 187(10):1–16 DOI: 10.1007/s10661-015-4797-7
12. Kelley WP (1963) Use of saline irrigation water. Soil Sci 95(6):385–391 DOI: 10.1097/00010694-196306000-00003
13. Kirda C (1997) Assessment of irrigation water quality. Options Mediterraneennes. Serie A: Seminaires Mediterraneens (CIHEAM)
14. Kisi O, Tombul M, Kermani MZ (2015) Modeling soil temperatures at different depths by using three different neural computing techniques. Theoret Appl Climatol 121(1–2):377–387 DOI: 10.1007/s00704-014-1232-x
15. Krishnan MS (1982) Geology of India and Burma, 6th edn. CBS Publisher and Distributors, New Delhi
16. Maqsood I, Khan MR, Huang GH, Abdalla R (2005) Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada. Eng Appl Artif Intell 18(1):115–125 DOI: 10.1016/j.engappai.2004.08.019
17. Nourani V, Baghanam AH, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243 DOI: 10.1016/j.jhydrol.2012.10.054
18. Raghunath HM (1987) Groundwater. Wiley Eastern Ltd, Delhi India
19. Richards Lorenzo Adolph (1954) Diagnosis and improvement of saline and alkali soils. Soil Sci 78(2):154 DOI: 10.1097/00010694-195408000-00012
20. Rojas R (1996) A short proof of the posterior probability property of classifier neural networks. Neural Comput 8(1):41–43 DOI: 10.1162/neco.1996.8.1.41
21. Sreekanth PD, Geethanjali N, Sreedevi PD, Ahmed S, Kumar NR, Jayanthi PK (2009) Forecasting groundwater level using artificial neural networks. Curr Sci 96(7):933–939
22. Strik DP, Domnanovich AM, Zani L, Braun R, Holubar P (2005) Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB neural network toolbox. Environ Model Softw 20(6):803–810 DOI: 10.1016/j.envsoft.2004.09.006