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Title: Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management
Authors: Kouziokas, Georgios N.
Chatzigeorgiou, Alexander
Perakis, Konstantinos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Engineering and technology::Environmental engineering
Issue Date: Dec-2018
Publisher: SpringerLink
Source: Water Resources Management
Volume: 32
Issue: 15
First Page: 5041
Last Page: 5052
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.
ISSN: 0920-4741
Other Identifiers: 10.1007/s11269-018-2126-y
Appears in Collections:Department of Applied Informatics

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