Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

Georgios N. Kouziokas, Alexander Chatzigeorgiou and Konstantinos Perakis
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Alexander Chatzigeorgiou: University of Macedonia
Konstantinos Perakis: University of Thessaly

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 15, No 14, 5052 pages

Abstract: Abstract Managing the groundwater resources is very vital for human life. This research proposes a methodology for predicting the groundwater levels which can be very valuable in water resources management. This study investigates the application of multilayer feed forward network models for forecasting the groundwater values in the region of Montgomery country in Pennsylvania. Multiple training algorithms and network structures were investigated to develop the best model in order to forecast the groundwater levels. Several multilayer feed forward models were created in order to be tested for their performance by changing the network topology parameters so as to find the optimal prediction model. The forecasting models were developed by applying different structures regarding the number of the neurons in every hidden layer and the number of the hidden network layers. The final results have shown a very good forecasting accuracy of the predicted groundwater levels. This research can be very valuable in water resources and environmental management.

Keywords: Artificial intelligence; Neural networks; Environmental management; Public management; Water level prediction; Water resources management (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11269-018-2126-y

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