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

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 b...

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Published in:	Water resources management Vol. 29; no. 15; pp. 5521 - 5532
Main Authors:	Mohanty, S., Jha, Madan K., Raul, S. K., Panda, R. K., Sudheer, K. P.
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.12.2015 Springer Nature B.V
Subjects:	Algorithms Aquifers Artificial neural networks Atmospheric Sciences Back propagation Civil Engineering Earth and Environmental Science Earth Sciences Environment Evaporation Forecasting Forecasting techniques Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater levels Hydrogeology Hydrology Hydrology/Water Resources Irrigation Lead time Learning theory Mathematical models Monsoons Neural networks Pan evaporation Parameter estimation Rain River basins Rivers Statistical analysis Studies Sustainable use Water management Water resources Water resources management Wind Neural network modeling Backpropagation GDX algorithm Alluvial aquifer system Groundwater-level forecasting
ISSN:	0920-4741, 1573-1650
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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.
AbstractList	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. 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.
Author	Jha, Madan K. Raul, S. K. Sudheer, K. P. Mohanty, S. Panda, R. K.
Author_xml	– sequence: 1 givenname: S. surname: Mohanty fullname: Mohanty, S. email: smohanty.wtcer@gmail.com organization: ICAR-Indian Institute of Water Management – sequence: 2 givenname: Madan K. surname: Jha fullname: Jha, Madan K. organization: AgFE Department, IIT Kharagpur – sequence: 3 givenname: S. K. surname: Raul fullname: Raul, S. K. organization: College of Agricultural Engineering &Technology, AAU – sequence: 4 givenname: R. K. surname: Panda fullname: Panda, R. K. organization: ICAR-Indian Institute of Water Management – sequence: 5 givenname: K. P. surname: Sudheer fullname: Sudheer, K. P. organization: Department of Civil Engineering, IIT Madras
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SubjectTerms	Algorithms Aquifers Artificial neural networks Atmospheric Sciences Back propagation Civil Engineering Earth and Environmental Science Earth Sciences Environment Evaporation Forecasting Forecasting techniques Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater levels Hydrogeology Hydrology Hydrology/Water Resources Irrigation Lead time Learning theory Mathematical models Monsoons Neural networks Pan evaporation Parameter estimation Rain River basins Rivers Statistical analysis Studies Sustainable use Water management Water resources Water resources management Wind
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Title	Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites
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