TY - JOUR
T1 - Development of wavelet network model for accurate water levels prediction with meteorological effects
AU - El-Diasty, Mohammed
AU - Al-Harbi, Salim
N1 - Funding Information:
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia , under grant no. 1434/980/266 (phase 2). The authors, therefore, acknowledge with thanks DSR technical and financial support. Also, the Yarmouth and Saint John tide gauges data was provided by the Marine Environmental Data Service (MEDS) of the Department of Fisheries and Oceans (DFO), Ottawa, Canada. The Ras Tanour and Jizan tide gauges data was provided by General Commission of Survey (GCS), Riyadh, KSA.
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - Accurate water levels modeling and prediction is essential for safety of coastal navigation and other maritime applications. Water levels modeling and prediction is traditionally developed using the least-squares-based harmonic analysis method that estimates the harmonic constituents from the measured water levels. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the current state-of-the-art artificial neural network has recently been developed for water levels prediction from short water level measurements. However, a highly nonlinear and efficient wavelet network model is proposed and developed in this paper for water levels modeling and prediction using short water level measurements. Water level measurements (about one month and a week) from six different tide gauges are employed to develop the proposed model and investigate the atmospheric changes effect. It is shown that the majority of error values, the differences between water level measurements and the modeled and predicted values, fall within the -5. cm and +5. cm range and root-mean-squared (RMS) errors fall within 1-6. cm range. A comparison between the developed highly nonlinear wavelet network model and the harmonic analysis method and the artificial neural networks shows that the RMS of the developed wavelet network model when compared with the RMS of the harmonic analysis method is reduced by about 70% and when compared with the RMS of the artificial neural networks is reduced by about 22%. It is also worth noting that if the atmospheric changes effect (meteorological effect) of the air pressure, the air temperature, the relative humidity, wind speed and wind direction are considered, the performance accuracy of the developed wavelet network model is improved by about 20% (based on the estimated RMS values).
AB - Accurate water levels modeling and prediction is essential for safety of coastal navigation and other maritime applications. Water levels modeling and prediction is traditionally developed using the least-squares-based harmonic analysis method that estimates the harmonic constituents from the measured water levels. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the current state-of-the-art artificial neural network has recently been developed for water levels prediction from short water level measurements. However, a highly nonlinear and efficient wavelet network model is proposed and developed in this paper for water levels modeling and prediction using short water level measurements. Water level measurements (about one month and a week) from six different tide gauges are employed to develop the proposed model and investigate the atmospheric changes effect. It is shown that the majority of error values, the differences between water level measurements and the modeled and predicted values, fall within the -5. cm and +5. cm range and root-mean-squared (RMS) errors fall within 1-6. cm range. A comparison between the developed highly nonlinear wavelet network model and the harmonic analysis method and the artificial neural networks shows that the RMS of the developed wavelet network model when compared with the RMS of the harmonic analysis method is reduced by about 70% and when compared with the RMS of the artificial neural networks is reduced by about 22%. It is also worth noting that if the atmospheric changes effect (meteorological effect) of the air pressure, the air temperature, the relative humidity, wind speed and wind direction are considered, the performance accuracy of the developed wavelet network model is improved by about 20% (based on the estimated RMS values).
KW - Harmonic analysis method
KW - Meteorological effects
KW - Neural networks
KW - Prediction
KW - Tide gauges
KW - Water levels
KW - Wavelet network
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U2 - 10.1016/j.apor.2015.09.008
DO - 10.1016/j.apor.2015.09.008
M3 - Article
AN - SCOPUS:84944883960
SN - 0141-1187
VL - 53
SP - 228
EP - 235
JO - Applied Ocean Research
JF - Applied Ocean Research
ER -