Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

Water Resources Management

Volumn 29, Issue 15, 2015, Pages 5521-5532

  Mohanty, S ^a   Jha, Madan K ^b   Raul, S K ^c   Panda, R K ^a   Sudheer, K P ^d  

^a ICAR INDIAN INSTITUTE OF WATER MANAGEMENT   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY   (India)

^c CAET AAU   (India)

^d INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Alluvial aquifer system; Backpropagation GDX algorithm; Groundwater level forecasting; Neural network modeling

Indexed keywords

AQUIFERS; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; FORECASTING; GROUNDWATER; NEURAL NETWORKS; WATER LEVELS; WATER MANAGEMENT;

ADAPTIVE LEARNING RATES; ALLUVIAL AQUIFER SYSTEMS; ARTIFICIAL NEURAL NETWORK APPROACH; GROUNDWATER LEVEL FORECASTING; LAND AND WATER MANAGEMENT; MODEL PERFORMANCE; NEURAL NETWORK MODEL; STATISTICAL INDICATORS;

GROUNDWATER RESOURCES;

ALGORITHM; AQUIFER; ARTIFICIAL NEURAL NETWORK; FLOW MODELING; GROUNDWATER; HYDROLOGICAL MODELING; WATER LEVEL; WATER MANAGEMENT;

EID: 84944683850     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-015-1132-6     Document Type: Article

References (33)

1. Aziz ARA, Wong KFV (1992) Neural network approach to the determination of aquifer parameters. Ground Water 30(2):164–166
2. Balkhair KS (2002) Aquifer parameters determination for large diameter wells using neural network approach. J Hydrol 265(1):118–128
3. Banerjee P, Singh VS, Chattopadhyay K, Chandra PC, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398(3–4):212–220
4. Chang F, Chen P, Liu C, Liao VH, Liao C (2013) Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling. J Hydrol 499:265–274
5. Coppola E, Szidarovszky F, Poulton M, Charles E (2003) Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. J Hydrol Eng ASCE 8(6):348–360
6. Coppola EA, Rana AJ, Poulton MM, Szidarovszky F, Uhl VW (2005) A neural network model for predicting aquifer water level elevations. Ground Water 43(2):231–241
7. Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896
8. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural network. J Hydrol 309:229–240
9. Emamgholizadeh S, Moslemi K, Karami G (2014) Prediction the groundwater level of Bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water Resour Manag 28:5433–5446
10. Garcia LA, Shigdi A (2006) Using neural networks for parameter estimation in ground water. J Hydrol 318(1–4):215–231
11. Ghose DK, Panda SN, Swain PC (2010) Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks. J Hydrol 394:296–304
12. Gobindraju RS, Ramachandra Rao A (2000) Artificial neural network in hydrology. Kluwer Academic Publishing, The Netherlands
13. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs
14. He Z, Zhang Y, Guo Q, Zhao X (2014) Comparative study of artificial neural networks and wavelet artificial neural networks for groundwater depth data forecasting with various curve fractal dimensions. Water Resour Manag 28:5297–5317
15. Hong YS, Rosen MR (2001) Intelligent characterization and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network. Urban Water 3(3):193–204
16. Karahan H, Ayvaz MT (2008) Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks. Hydrogeol J 16:817–827
17. Krishna B, Rao YRS, Vijaya T (2008) Modeling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrol Process 22:1180–1188
18. Kuo V, Liu C, Lin K (2004) Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res 38(1):148–158
19. Maier HR, Dandy GC (1998) Understanding the behavior and optimizing the performance of backpropagation neural networks: an empirical study. Environ Model Softw 13:179–191
20. Maier HR, Dandy GC (2000) Neural networks for prediction and forecasting of water resources variables: a review of modeling issue and application. Environ Model Softw 15:101–124
21. Milot J, Rodriguez MJ, Serodes JB (2002) Contribution of neural networks for modeling trihalomethanes occurrence in drinking water. J Water Resour Plan Manag ASCE 128(5):370–376
22. Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resour Manag 24:1845–1865
23. Morshed J, Kaluarachchi JJ (1998) Parameter estimation using artificial neural network and genetic algorithm for free-product migration and recovery. Water Resour Res 34(5):1101–1113
24. Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 20:77–90
25. Sahoo S, Jha MK (2013) Groundwater level prediction using multiple linear regression and artificial neural network techniques. Hydrogeol J 21(8):1865–1887
26. Samani M, Gohari-Moghadam M, Safavi AA (2007) A simple neural network model for the determination of aquifer parameters. J Hydrol 340:1–11
27. Shigdi A, Garcia LA (2003) Parameter estimation in groundwater hydrology using artificial neural networks. J Comput Civ Eng ASCE 17(4):281–289
28. Taormina R, Chau K, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intell 25(8):1670–1676
29. ASCE Task Committee (2000a) Artificial neural networks in hydrology- I: preliminary concepts. J Hydrol Eng ASCE 5(2):115–123
30. ASCE Task Committee (2000b) Artificial neural networks in hydrology- II: hydrologic applications. J Hydrol Eng ASCE 5(2):124–137
31. Uddameri V (2007) Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas. Environ Geol 51:885–895
32. Viveros UI, Parra JO (2014) Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well log data. J Appl Geophys 107:45–54
33. Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396:128–138