Abstract
Reliable forecast of groundwater level is necessary for its sustainable use and for planning land and water management strategies. This paper deals with an application of artificial neural network (ANN) approach to the weekly forecasting of groundwater levels in multiple wells located over a river basin. Gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm was employed to predict groundwater levels 1 week ahead at 18 sites over the study area. Based on the domain knowledge and pertinent statistical analysis, appropriate set of inputs for the ANN model was selected. This consisted of weekly rainfall, pan evaporation, river stage, water level in the surface drain, pumping rates of 18 sites and groundwater levels of 18 sites in the previous week, which led to 40 input nodes and 18 output nodes. During training of the ANN model, the optimum number of hidden neurons was found to be 40 and the model performance was found satisfactory (RMSE = 0.2397 m, r = 0.9861, and NSE = 0.9722). During testing of the model, the values of statistical indicators RMSE, r and NSE were 0.4118 m, 0.9715 and 0.9288, respectively. Using the same inputs, the developed ANN model was further used for forecasting groundwater levels 2, 3 and 4 weeks ahead in 18 tubewells. The model performance was better while forecasting groundwater levels at shorter lead times (up to 2 weeks) than that for larger lead times.
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Aziz ARA, Wong KFV (1992) Neural network approach to the determination of aquifer parameters. Ground Water 30(2):164–166
Balkhair KS (2002) Aquifer parameters determination for large diameter wells using neural network approach. J Hydrol 265(1):118–128
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
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
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
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
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
Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural network. J Hydrol 309:229–240
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
Garcia LA, Shigdi A (2006) Using neural networks for parameter estimation in ground water. J Hydrol 318(1–4):215–231
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
Gobindraju RS, Ramachandra Rao A (2000) Artificial neural network in hydrology. Kluwer Academic Publishing, The Netherlands
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs
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
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
Karahan H, Ayvaz MT (2008) Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks. Hydrogeol J 16:817–827
Krishna B, Rao YRS, Vijaya T (2008) Modeling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrol Process 22:1180–1188
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
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
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
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
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
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
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
Sahoo S, Jha MK (2013) Groundwater level prediction using multiple linear regression and artificial neural network techniques. Hydrogeol J 21(8):1865–1887
Samani M, Gohari-Moghadam M, Safavi AA (2007) A simple neural network model for the determination of aquifer parameters. J Hydrol 340:1–11
Shigdi A, Garcia LA (2003) Parameter estimation in groundwater hydrology using artificial neural networks. J Comput Civ Eng ASCE 17(4):281–289
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
ASCE Task Committee (2000a) Artificial neural networks in hydrology- I: preliminary concepts. J Hydrol Eng ASCE 5(2):115–123
ASCE Task Committee (2000b) Artificial neural networks in hydrology- II: hydrologic applications. J Hydrol Eng ASCE 5(2):124–137
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
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
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
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Mohanty, S., Jha, M.K., Raul, S.K. et al. Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites. Water Resour Manage 29, 5521–5532 (2015). https://doi.org/10.1007/s11269-015-1132-6
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DOI: https://doi.org/10.1007/s11269-015-1132-6