Artificial Neural Networks As Watershed Nutrient Loading Models
| dc.contributor.author | Kim, Raymond | en_US |
| dc.contributor.chair | Loucks, Daniel Peter | en_US |
| dc.contributor.committeeMember | Bisogni Jr, James John | en_US |
| dc.date.accessioned | 2012-06-28T20:57:32Z | |
| dc.date.available | 2016-12-30T06:47:02Z | |
| dc.date.issued | 2011-08-31 | en_US |
| dc.description.abstract | Artificial neural network (ANN) is a computing architecture in the area of artificial intelligence. The present study aims at the wider use of ANN in the watershed loading prediction. An important aspect of these initiatives is the accurate forecasting nutrient load in runoff water. Accurate prediction of watershed loading has been recognized as an important measure for effective water management strategy. This study compares Haith's Generalized Watershed Loading Function (GWLF) and Arnold's Soil and Water Assessment Tool (SWAT) to multilayer artificial neural networks for monthly/daily watershed load forecast modeling. The comparison splits into two different parts; 1) performance of the ANN calibrated to the observed data; 2) performance of the ANN calibrated to simulated data. The first part includes comparison of model estimates from both the ANN and the GWLF to the collected observations from West Branch Delaware River watershed. The second part evaluates the performance of calibrated ANN model using simulated output using the SWAT. Feed-forward networks with one hidden layer of sigmoid nodes followed by an output layer of linear nodes were created for both comparisons. The model performances were evaluated using various statistical indices. For each of the ANN models, different numbers of nodes were tested in order to create the optimal structure. The modeling results indicate that calibrated feed-forward ANN models were found to provide reasonable prediction accuracy as the GWLF and the SWAT in most of the nutrient categories. With its flexibility and computation efficiency, the ANN is anticipated as a useful tool to obtain a quick preliminary assessment of nutrient loading variations. | en_US |
| dc.identifier.other | bibid: 7745378 | |
| dc.identifier.uri | https://hdl.handle.net/1813/29469 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Artificial Neural Networks | en_US |
| dc.subject | Watershed Nutrient Loading | en_US |
| dc.subject | West Basin Delaware River Watershed | en_US |
| dc.title | Artificial Neural Networks As Watershed Nutrient Loading Models | en_US |
| dc.type | dissertation or thesis | en_US |
| thesis.degree.discipline | Civil and Environmental Engineering | |
| thesis.degree.grantor | Cornell University | en_US |
| thesis.degree.level | Master of Science | |
| thesis.degree.name | M.S., Civil and Environmental Engineering |
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