Research On The Nonlinear Streamflow Forecast Models Using Wavelet Analysis And Relevance Vector Machine

As one of the most important facets in water resources research, streamflow forecast not only plays the great role in national economy, but also is the basis for crucial decisions, such as water resources planning and its comprehensive utilization, flood control, and hydropower station operation. Since streamflow forecast is a tough topic in both science and technology research area, low forecast precision, and less concern of hydrology system uncertainty, are the main obstacles in streamflow forecast, which lead to difficulities in supervising engineering practice. Thereby researchers at domain and abroad are always dedicated to seeking for effective methods to solve above problems. However, hydrology system is a complex and huge system, and its high nonlinearity and uncertainty when changing in space and time make it hard to describe and resolve proposed models, which are lake of satisfying results so far and starve for new theories and technology. Consequently, the research of advanced theory and technology of streamflow forecast is a hot science topic all the time. Based on the ananlysis of nonlinear hydrology factors, and uncertain historical records and modeling, the thesis explores thorough streamflow forecast research by adopting modern nonlinear scientific techniques. Focusing on the built nonlinear streamflow forecast models, biased wavelet neural network, improved adaptive Metropolis algorithm, and uncertain research frame using relevance vector machine are put forward, which exhibit the performance of wavelet neural network in managing data samples amd reducing calculation redundancy, testify the validity of algorithm in parameter estimation, acquire the predominance of relevance vector machin in treating with uncertain information, and develop streamflow forecast theory. The research results are successfully applied in engineering practice of streamflow forecast in Fengtan and Three Gorges valleys, and supplies good references for hydropower optimal regulation and decision-makers. The study work and innovations are listed as follows:By studying the nonlinear modeling abilities of wavelet analysis theory and relevance vector machine, annual streamflow forecast model is established with the combination of wavelet analysis and AR, as the wavelet decomposition can reveal the details of streamflow time series and explore their evolving process and characteristics. The simulation results show that such coupled method supplies good forecasts results, which are also better than single AR. According to the application of wavelet theory into"Choangyang Water", trends of the streamflow time series are detected. The uncertainty analysis capability of relevance vector machine is then validated by function regression example.Biased wavelet neural network (BWNN) and its corresponding learning algorithm are proposed for the purpose of dealing with the redundancy in wavelet transform. BWNN is an adaptive wavelet network constructed by biased wavelets, and the form of such wavelets can adapt to special applications during learning period, while not just regulate the parameters of fixed wavelets, which reduce the redundancy to certain extent. The presented learning algorithm based on gradient decent also holds the treats of simple, clarity and efficiency. When applied to month streamflow forecast of Fengtan reservoir, BWNN can not only reach the goal of increasing forecast precision, but also avoid falling into local minimum and arising oscillation, which furnishes a new effective approach to nonlinear forecast modeling.Aiming at resolving the issue of designing appropriate"proposal distribution"in Markov Chain Monte Carlo (MCMC), an improved adaptive Metropolis algorithm (IAM) is developed in the thesis. IAM selects normal density distribution to sample in objective function, and ameliorates the way of computing covariance matrix, which accelerate the algorithm convergence speed and lessen uncertainty factors influence. The achievements enrich the MCMC theory, and as well as provide a powerful tool for streamflow frequency forecast.Parameter estimation of Pearson-III distribution using IAM is brought forward to figure out the problems of curve selection and parameter estimation in streamflow frequency forecast. IAM can obtain certain useful statistical information such as posterior distribution of parameters. By keeping the richness of sample data, IAM is able to reduce the probability of converging at the local optimal area, which advances the quality and speed of final estimators. Based on the instances of annual and a period of ten days streamflow frequency calculation in Fengtan reservoir, satisfying results are obtained, and can reflect the forecast range related to uncertainty of model parameters, which simultaneously verify the better performance of IAM.The research frame of stramflow forecast uncertainty based on relevance vector machine (RVM) is established in allusion to the less attention of model uncertainty in most nonlinear forecast methods. With the analysis of components in streamflow time series, the frame is composed of wavelet denoising, RVM modeling, model selection based on IAM, and multi-step forecast by recursive calculation. The proposed frame avails of the prominent characteristics of RVM in outputting forecast distribution, using less relevance vectors, and confining no limitation to kernel function. Further more, the valid presented model selection method implements the trade-off between training precision and model complexity. The application to daily streamflow forecast of Yichan station indicated that, the frame is capable of supplying good forecast results, and the equipped uncertainty distribution offers more information for reservoir regulation decision, which redounds to quantitatively estimate different risks when concerning forecast uncertainty.