Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks

2008 ◽  
pp. 127-138 ◽  
Author(s):  
K. Mohammadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Table ◽  
Artificial Neural

scholarly journals Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

2016 ◽  
Vol 20 (4) ◽  
pp. 1405-1412 ◽  
Author(s):  
Yabin Sun ◽  
Dadiyorto Wendi ◽  
Dong Eon Kim ◽  
Shie-Yui Liong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Groundwater Resources ◽  
Computational Cost ◽  
Groundwater Table ◽  
Physical Parameters ◽  
Swamp Forest ◽  
Freshwater Swamp ◽  
Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days.

scholarly journals Technical Note: Application of artificial neural networks in groundwater table forecasting – a case study in Singapore swamp forest

2015 ◽  
Vol 12 (9) ◽  
pp. 9317-9336 ◽  
Author(s):  
Y. Sun ◽  
D. Wendi ◽  
D. E. Kim ◽  
S.-Y. Liong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Groundwater Resources ◽  
Computational Cost ◽  
Technical Note ◽  
Groundwater Table ◽  
Physical Parameters ◽  
Swamp Forest ◽  
Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a swamp forest of Singapore. A standard multilayer perceptron (MLP) is selected, trained with the Levenberg–Marquardt (LM) algorithm. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce accurate forecast with a leading time up to 7 days, whereas the performance slightly decreases when leading time increases.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural
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