Query: BP neural network tide

title: Application of BP Neural Network in Monitoring of Ocean Tide Level
abstract: Accurate monitor and prediction of tide levels under severe weather, such as typhoons, is very important for human activities in oceanic and coastal areas. When predicting tide levels, non astronomical components may play a same critical role as that of astronomical components. The traditional prediction methods and models based on the analysis of astronomical components do not work well in these situations. The objective of this paper is to apply BP neutral network method to monitor and predict ocean tide level under the influence of a typhoon in consideration of non astronomical components. With five year tide records under the influence of typhoons at Luchaogang China, the proposed BP model is verified to be highly accurate and acceptable to monitor and predict ocean tide level with a typhoon effect, and the results reveal that the BP neural network method can provide a solid basis for tidal level prediction and typhoon disaster reduction.

title: Tide prediction using tide observation at a nearby site based on BP neural network
abstract: Based on a BP neural network(NN) model, we investigated the possibility to derive the tide level at one site using the tide observation at a nearby site. As an example, the application of the neural network model to predict the tide level at Station Sanya using the observed data at Station Xisha is presented. The observed tide level at different hours ahead of the prediction time(t N+1, t 1, t) are used as the input vector. Combinations of different numbers of input layer nodes(2 to 10) and hidden layer nodes(3, 4, 5, 10, 15) are used, and the outputs are compared with field data. For the specific case used in this study, four nodes in the hidden layer lead to the best prediction while 15 nodes performs the worst; two nodes in the input vector are the most suitable while more input nodes lead to degraded performance. The model setup with a hidden layer of four nodes and an input vector of two variables(t 1, t) has the best prediction accuracy in this case, with a correlation coefficient of 0.9901, a root mean square(RMS) error of 0.06 m, and a prediction error of 0.16~0.15 m(between prediction and observation) The proposed NN model is examined for its applicable to predict tides at a station using the observed tide data of a nearby station if they are physically coherent with similar tidal type.

title: A Tidal Level Prediction Approach Based on BP Neural Network and Cubic B Spline Curve with Knot Insertion Algorithm
abstract: Tide levels depend on both long term astronomical effects that are mainly affected by moon and sun and short term meteorological effects generated by severe weather conditions like storm surge. Storm surge caused by typhoons will impose serious security risks and threats on the coastal residents' safety in production, property, and life. Due to the challenges of nonperiodic and incontinuous tidal level record data and the influence of multimeteorological factors, the existing methods cannot predict the tide levels affected by typhoons precisely. This paper targets to explore a more advanced method for forecasting the tide levels of storm surge caused by typhoons. First, on the basis of successive five year tide level and typhoon data at Luchaogang, China, a BP neural network model is developed using six parameters of typhoons as input parameters and the relevant tide level data as output parameters. Then, for an improved forecasting accuracy, cubic B spline curve with knot insertion algorithm is combined with the BP model to conduct smooth processing of the predicted points and thus the smoothed prediction curve of tidal level has been obtained. By using the data of the fifth year as the testing sample, the predicted results by the two methods are compared. The experimental results have shown that the latter approach has higher accuracy in forecasting tidal level of storm surge caused by typhoons, and the combined prediction approach provides a powerful tool for defending and reducing storm surge disaster.

title: A Kind of Short term Height Prediction of Tidal Level of Rivers Based on BP Neural Network Model
abstract: In comparison between the tide bore of rivers and the tide of ocean,the occurrence time and height of the river tide bore has the same feature as the ocean tide;but the river tide bore is influenced more easily by astronomy,weather and other factors,so it has the instability that is different from the ocean tide;the tide level of the tidal bore is the comprehensive result influenced by all kinds of factors.Since the BP neural network can approach any non linear function,a short term prediction model of the tidal height has been constructed.Some historical tidal data,as a training sample,are used to train the model,and others are used to predict the tidal data.Finally,the validity and the feature of the prediction model have been verified.

title: Urbanization level forecast based on BP neural network
abstract: Urbanization is the general trend and tide of the current world development and also one of the most remarkable social and economical phenomena in the world. Urbanization level becomes an important symbol of the economic strength and modernization level in a region and thus how to improve the local urbanization level has become a priority for economic development. With the increasing urbanization level, a series of adverse effects have been brought about. It is a very important task to make a scientific forecast for the urbanization of a region. Take Zhejiang Province as an example; only considering the present economic development factors, the currently most popular BP network (Back Propagation Network) in the neural network is adopted to establish the forecast model; and the urbanization level of Zhejiang Province in 2000 2004 is forecasted. For the forecast result, the maximum relative error is 2.51% the minimum relative error is 0.23% and the mean absolute percent error is 1.05% Thus, the result indicates that the model has high forecast precision and therefore can be used to forecast the urbanization level of Zhejiang Province in the future.

title: The BP Neural Network Technology Application in the Prediction of the Redtide
abstract: A red tide as an sananomalous ecological phenomenon is characterized with abruptness and nonlinearness,and red tide prediction has been a hot spot in ocean ographic studies.In this paper,by using the principle of fuzzy mathematics,we developed a computer program to simulate the BP model on the basis of Visual C~ language.Engineering analysis shows the same result between the simulation and the observation of red tide which is practical.The calculation procedures are simple and easy.

title: Application of BP neural network in tidal level short term forecast of Wusong estuary
abstract: Combining with the regularity of tide data,the paper sets up non linear relation among the hourly tidal levels of Wusong estuary,does short term forecast of tidal level using BP neural network.The result shows the accuracy of forecast could meet the standard for hydrological information and hydrological forecasting during non storm surge period,especially the accuracy of forecast within 6 hours is high.

title: The Application of BP Neural Network in the Prediction of Chlorophyll a Concentration in the Bohai Gulf
abstract: By using BP Neural Network method,a nonlinear relationship between various physical chemical factors and the chlorophyll a concentration in the Bohai Gulf is established.The simulated results show that the predicted chlorophyll a concentration fits the observed data well,indicating that this model can be indirectly used for local red tide prediction.

title: Harmful red tide warning based on general regression neural network
abstract: This paper discusses the theory and algorithm of general regression neural network, applies it into red tide early warning, and compares general regression neural network with the widely used BP neural network. After research, the results show that general regression neural network in red tide warning is better than the BP neural network, and is worthy for practical use.

title: Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea
abstract: In multi resolution analysis (MRA) by wavelet function Daubechies (db) we decompose the signal in two parts, the low and high frequency contents. We remove the high frequency content and reconstruct a new "de noise" signal by using inverse wavelet transform. The calculation of tidal constituent phase lags was made to determine the input and output data patterns used in building network structure of Artificial Neuron Network (ANN) model. The "de noise" signal was, then, used as the input data to improve the forecasting accuracy of the ANN model. The wavelet spectrum, conventional energy spectrum (fast Fourier transform, FFT) and harmonic analysis were used to analyze the characteristics of tidal data. Using only a very short period data as a training data set in Artificial Neuron Network Back Propagate (ANN BP) model, the developed ANN+Wavelet model can accurately predict or supply the missing tide data for a long period (1 5 years) The results also show that the concept of tidal constituent phase lags can improve ANN model of tidal forecasting and data supplement. The addition of the wavelet analysis to ANN method can prominently improve the prediction quality.