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A platform for research: civil engineering, architecture and urbanism
GROUNDWATER LEVEL FORECASTING USING FEED FORWARD NEURAL NETWORK TRAINED WITH DIFFERENT ALGORITHMS
Feed forward neural networks have been applied successfully in the field of hydrology. They are easy to handle and can approximate any input/output map. The performance of fourteen types of algorithms trained with feed forward neural network is examined to determine the combination that can predict the groundwater levels accurately and efficiently using a relatively short length of groundwater level records. Tirupati, located in Chittoor district of the drought-prone Rayalaseema region of Andhra Pradesh, South India was chosen as the study area as its groundwater levels declined in the last decade due to overexploitation. Results reveal that feed forward neural network trained with Levenberg-Marquardt algorithm predicts well the groundwater levels.
GROUNDWATER LEVEL FORECASTING USING FEED FORWARD NEURAL NETWORK TRAINED WITH DIFFERENT ALGORITHMS
Feed forward neural networks have been applied successfully in the field of hydrology. They are easy to handle and can approximate any input/output map. The performance of fourteen types of algorithms trained with feed forward neural network is examined to determine the combination that can predict the groundwater levels accurately and efficiently using a relatively short length of groundwater level records. Tirupati, located in Chittoor district of the drought-prone Rayalaseema region of Andhra Pradesh, South India was chosen as the study area as its groundwater levels declined in the last decade due to overexploitation. Results reveal that feed forward neural network trained with Levenberg-Marquardt algorithm predicts well the groundwater levels.
GROUNDWATER LEVEL FORECASTING USING FEED FORWARD NEURAL NETWORK TRAINED WITH DIFFERENT ALGORITHMS
Pradeep Kumar, G. N. (author) / Sujatha, P. (author) / Srinivas, P. (author)
ISH Journal of Hydraulic Engineering ; 16 ; 20-35
2010-01-01
16 pages
Article (Journal)
Electronic Resource
Unknown
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