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Abstract:Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non-steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two-stage genetic algorithms were used to construct nonlinear time series models of 10-day streamflows of the Wu-Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications.

Abstract:Much of the nonlinearity and uncertainty regarding the flood process is because hydrologic data required for estimation are often tremendously difficult to obtain. This study employed a back-propagation network (BPN) as the main structure in flood forecasting to learn and to demonstrate the sophisticated nonlinear mapping relationship. However, a deterministic BPN model implies high uncertainty and poor consistency for verification work even when the learning performance is satisfactory for flood forecasting. Therefore, a novel procedure was proposed in this investigation which integrates linear transfer function (LTF) and self-organizing map (SOM) to efficiently determine the intervals of weights and biases of a flood forecasting neural network to avoid the above problems. A SOM network with classification ability was applied to the solutions and parameters of the BPN model in the learning stage, to classify the network parameter rules and to obtain the winning parameters. The outcomes from the previous stage were then used as the ranges of the parameters in the recall stage. Finally, a case study was carried out in Wu-Shi basin to demonstrate the effectiveness of the proposal.

Zhou, Z.; Liu, S.; Hua H.; Chen, C.S.; Zhong G.; Lin, H., and Huang, C.W., 2014. Frequency analysis for predicting extreme precipitation in Changxing station of Taihu Rainfall induced flooding is one of the most severe natural disasters in coastal regions. In recent years, along with global warming and sea level rising, extreme hydrological events, such as extreme precipitation, are of high occurrence. Meanwhile, rapid urbanization makes the urban environment transformed dramatically and results in additional flood ventures. Taihu Basin, located in Yangtze Delta, is the richest basin in China and flood control planning in this region is of high significance. Therefore, precipitation analysis, as a basic work of flood design, should be accurate and precise. In this study, based on precipitation data at Changxing Station of Taihu Basin, precipitation frequency analysis using mixed methods is performed. Two of the most applied distribution models, Pearson-III (P3) and Generalized Extreme Value (GEV), are investigated. For parameter estimation method of probability distribution functions, maximum likelihood estimation (MLE) and L-moments (LM) are used. In addition, seeking-matrix curve fitting based on conventional moments (CM) is also investigated to compare the calculation results. The performance of mixed methods is tested by two classical goodness-of-fit tests, Chi-Square test and K-S test. Consequently, GEV distribution model based on LM is evaluated to be the best fitting model for identifying and predicting future precipitation occurrence. So precipitation estimations from different return periods at Changxing Station are identified. This study is a new attempt to precipitation frequency analysis in the stations of Taihu Basin and the result can provide a reference for flood risk and water resource management in Taihu Basin and even in more other regions in China. ADDITIONAL INDEX WORDS:Maximum daily precipitation, frequency analysis, parameter estimation, Taihu Basin.

In order to estimate water supply potential, the effects of shortages on water users, and the uncertainty of local headspring conditions during the planning stage of reservoir construction, the Shortage Index (SI) is often employed. However, the criterion used in the SI is difficult to adjust to satisfy local conditions and objectives. The SI also employs an ambiguous definition of value. Thus, this study adopted a water supply reliability index (WSRI) as an alternative to the SI for providing the criterion for water resources project planning. The value of the WSRI is easily understood, because it is defined according to the real water supply situation and it has a strong linear relationship with values of SI. For any given water supply system, the estimated results derived from this study could serve as an additional remark on different SI values to explain the relevant water supply considerations. In addition, for a new planning site, the estimated results of this study could provide another way for engineers to evaluate the maximum water supply capability. Consequently, an interesting avenue of investigation in future research would be the incorporation of the WSRI with the risk of deficit frequency in establishing an efficient and transparent bottom-up approach for water resources management, involving all the relevant stakeholders.