A hybrid model for numerical wave forecasting and its implementation-Ⅰ.The wind wave model

The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.

A hybrid model for short-term rainstorm forecasting based on a back-propagation neural network and synoptic diagnosis

Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network(BPNN)with synoptic diagnosis for predicting rainstorms,and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study.Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases,and the threat score(TS)of rainstorms was more than 0.75.However,the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%.The BPNN method efficiently forecasted these two rainstorm types;the TS and equitable threat score(ETS)of rainstorms were 0.80 and 0.79,respectively.The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception.This kind of hybrid model enhanced the forecasting accuracy of rainstorms.The findings of this study provide certain reference value for the future development of refined forecast models with local features.

A hybrid model for numerical wave forecasting and its implementation-Ⅱ .The discrete part and implementation of the model

In the first part of the present paper we have explained why we manage to formulate another wave prediction model when so many of them, including the so-called third generation model, have already been in use. The wind-wave part of the proposed model has also been given. Now we proceed to discuss the swell part,the implementation of the model as a prediction method,mumerical experiments done with ideal wind fields and hindcasts made in the Bohai Sea,in the neighboring seas adjacent to China and in the Northwest Pacific.

A hybrid forecasting model for depth-averaged current velocities of underwater gliders

In this paper,we propose a hybrid forecasting model to improve the forecasting accuracy for depth-averaged current velocities(DACVs) of underwater gliders.The hybrid model is based on a discrete wavelet transform(DWT),a deep belief network(DBN),and a least squares support vector machine(LSSVM).The original DACV series are first decomposed into several high-and one low-frequency subseries by DWT.Then,DBN is used for high-frequency component forecasting,and the LSSVM model is adopted for low-frequency subseries.The effectiveness of the proposed model is verified by two groups of DACV data from sea trials in the South China Sea.Based on four general error criteria,the forecast performance of the proposed model is demonstrated.The comparison models include some well-recognized single models and some related hybrid models.The performance of the proposed model outperformed those of the other methods indicated above.

A NEW HYBRID FORECASTING ALGORITHM AND ITS APPLICATION IN ECONOMIC ANALYSIS

There exists a great deal of periodic non-stationary processes in natural,social and eco- nomical phenomenon.It is very important to realize the dynamic analysis and real-time forecast within a period.In this letter,a wavelet-Kalman hybrid estimation and forecasting algorithm based on step-by-step filtering with the real-time and recursion property is put forward.It combines the advantages of Kalman filter and wavelet transform.Utilizing the information provided by multi- sensor effectively,this algorithm can realize not only real-time tracking and dynamic multi-step fore- casting within a period,but also the dynamic forecasting between periods,and it has a great value to the system decision-making.Simulation results show that this algorithm is valuable.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

In this paper,we propose a hybrid forecasting model(HFM)for the short-term electric load forecasting using artificial neural network(ANN),discrete Fourier transformation(DFT)and principal component analysis(PCA)techniques in order to attain higher prediction accuracy.Firstly,we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation.Approximate curves,together with other input variables,are given to the ANN to predict the next-day hourly load curves.Furthermore,we predict PCA scores to obtain approximate load curves in the first step,which are then given to the ANN again in the second step.Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads.Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018,we train our models using historical data since January 2008.The forecast results show that the HFM consisting of“ANN with DFT”and“ANN with PCA”predicts next-day hourly loads more accurately than the conventional three-layered ANN approach.Their corresponding mean average absolute errors show 2.7%for ANN with DFT,2.6%for ANN with PCA and 3.0%for the conventional ANN approach.We also find that in May,when electric demand is smaller with smaller fluctuations,forecasting errors are much smaller than January for all the models.Thus,we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.