𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Application of artificial neural networks in tide-forecasting

✍ Scribed by T.L. Lee; D.S. Jeng


Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
242 KB
Volume
29
Category
Article
ISSN
0029-8018

No coin nor oath required. For personal study only.

✦ Synopsis


An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has been based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters are required for the prediction of a long-term tidal level with harmonic analysis. Unlike conventional harmonic analysis, this paper presents an artificial neural network (ANN) model for forecasting the tidal-level using the short term measuring data. The ANN model can easily decide the unknown parameters by learning the input-output interrelation of the short-term tidal records. Three field data with three types of tides will be used to test the performance of the proposed ANN model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record.


πŸ“œ SIMILAR VOLUMES


Application of an artificial neural netw
✍ Gwo-Fong Lin; Lu-Hsien Chen πŸ“‚ Article πŸ“… 2005 πŸ› John Wiley and Sons 🌐 English βš– 163 KB

A neural network with two hidden layers is developed to forecast typhoon rainfall. First, the model configuration is evaluated using eight typhoon characteristics. The forecasts for two typhoons based on only the typhoon characteristics are capable of showing the trend of rainfall when a typhoon is

Artificial neural network for forecastin
✍ Abdallah Al-Shehri πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 173 KB πŸ‘ 2 views

An arti"cial neural network (ANN) model for forecasting the residential electrical energy (REE) in the Eastern Province of Saudi Arabia is presented. A comparison of the neural model with the polynomial "t is made for validation purposes. The results show that the forecasting of the REE predicted by