Minimum relative entropy theory for streamflow forecasting with frequency as a random variable     

Huijuan Cui  Vijay P. Singh 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(6):1545-1563

This paper develops a minimum relative entropy theory with frequency as a random variable, called MREF henceforth, for streamflow forecasting. The MREF theory consists of three main components: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. MREF is robust at determining the main periodicity, and provides higher resolution spectral density. The theory is evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflows show the coefficient of determination (r ²) of 0.876, which is slightly higher in the Upper Mississippi (r ² = 0.932) than in the Lower Mississippi (r ² = 0.806). Comparison of different priors shows that the prior with the background spectral density with a peak at 1/12 frequency provides satisfactory accuracy, and can be used to forecast monthly streamflow with limited information. Four different entropy theories are compared, and it is found that the minimum relative entropy theory has an advantage over maximum entropy (ME) for both spectral estimation and streamflow forecasting, if additional information as a prior is given. Besides, MREF is found to be more convenient to estimate parameters with cepstrum analysis than minimum relative entropy with spectral power as random variable (MRES), and less information is needed to assume the prior. In general, the reliability of monthly streamflow forecasting from the highest to the lowest is for MREF, MRES, configuration entropy (CE), Burg entropy (BE), and then autoregressive method (AR), respectively.  相似文献   

Synthesis of Non‐Toxic Triethyl Citrate Plasticizer by Esterification of Renewable Citric Acid Using Modified Zeolite          下载免费PDF全文

Kakasaheb Y. Nandiwale  Sanjay P. Borikar  Vijay V. Bokade 《洁净——土壤、空气、水》2015,43(6):927-931

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River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model     

Zhenghao?Zhang  Qiang?ZhangEmail author  Vijay?P.?Singh  Peijun?Shi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(9):2667-2682

Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins.  相似文献   

Nonstationarity-based evaluation of flood risk in the Pearl River basin: changing patterns,causes and implications   总被引：1，自引：1，他引：0  

Xihui Gu  Vijay P. Singh  Mingzhong Xiao  Jinqiang Cheng 《水文科学杂志》2017,62(2):246-258

Nonstationary GEV-CDN models considering time as a covariate are built for evaluating the flood risk and failure risk of the major flood-control infrastructure in the Pearl River basin, China. The results indicate: (1) increasing peak flood flow is observed in the mainstream of the West River and North River basins and decreasing peak flood flow is observed in the East River basin; in particular, increasing peak flood flow is detected in the mainstream of the lower Pearl River basin and also in the Pearl River Delta region, the most densely populated region of the Pearl River basin; (2) differences in return periods analysed under stationarity and nonstationarity assumptions are found mainly for floods with return periods longer than 50 years; and (3) the failure risks of flood-control infrastructure based on failure risk analysis are higher under the nonstationarity assumption than under the stationarity assumption. The flood-control infrastructure is at higher risk of flood and failure under the influence of climate change and human activities in the middle and lower parts of Pearl River basin.