AI SearchA neural network model of the salinity in the West Pearl River estuary
Call Number: AIT Thesis no. WM-01-04 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. WM-01-04Publication details: Bangkok : Asian Institute of Technology, 2002Description: 99, [28] leavesSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2002 Summary: Salinity intrusion plays an important role in human being, including the effects to the water supply for agriculture and domestic. Although this field of study drew the attention of scientist from a long time ago, using Artificial Neural Networks (ANNs) for prediction salinity distribution in the estuary is still rarely been done. Two neural network software packages (EasyNN and WinNN32) are presented in this study for forecasting salinity twelve hours ahead at six potential water abstraction locations along the West Pearl River Estuary. The data used for training and testing neural networks by these packages came from the results of a previous study using the three-dimensional hydrodynamic model RMA. The results for different types of ANNs applied by EasyNN and WinNN32 are compared. The WinNN32 with quick-prop algorithm was found more suitable for the salinity prediction than the EasyNN. The results also indicate that with appropriate structures, ANNs can give high performance for salinity prediction in estuary.
| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
20-AIT Publication
|
Asian Institute of Technology Library AIT Publications | AIT Thesis no. WM-01-04 (Browse shelf(Opens below)) | 1 | Available | 30050120593503 | |||||||||||||
|
20-AIT Publication
|
Asian Institute of Technology Library AIT Publications | AIT Thesis no. WM-01-04 (Browse shelf(Opens below)) | 2 | Available | 30050120593511 | |||||||||||||
40-Archives
|
Asian Institute of Technology Library Archives | AIT Thesis no. WM-01-04 (Browse shelf(Opens below)) | Available | 30050120236251 |
A thesis report submitted in partial fulfillment of the requirements for the degree of Master of Engineering
Thesis (M.Eng.) - Asian Institute of Technology, 2002
Salinity intrusion plays an important role in human being, including the effects to the water supply for agriculture and domestic. Although this field of study drew the attention of scientist from a long time ago, using Artificial Neural Networks (ANNs) for prediction salinity distribution in the estuary is still rarely been done. Two neural network software packages (EasyNN and WinNN32) are presented in this study for forecasting salinity twelve hours ahead at six potential water abstraction locations along the West Pearl River Estuary. The data used for training and testing neural networks by these packages came from the results of a previous study using the three-dimensional hydrodynamic model RMA. The results for different types of ANNs applied by EasyNN and WinNN32 are compared. The WinNN32 with quick-prop algorithm was found more suitable for the salinity prediction than the EasyNN. The results also indicate that with appropriate structures, ANNs can give high performance for salinity prediction in estuary.
There are no comments on this title.