Yu-Qi Li

DNA Optimization Threshold Autoregressive Prediction Model and Its Application in Ice Condition Time Series

There are many parameters which are very difficult to calibrate in the threshold autoregressive prediction model for nonlinear time series. The threshold value, autoregressive coefficients, and the delay time are key parameters in the threshold autoregressive prediction model. To improve prediction precision and reduce the uncertainties in the determination of the above parameters, a new DNA (deoxyribonucleic acid) optimization threshold autoregressive prediction model (DNAOTARPM) is proposed by combining threshold autoregressive method and DNA optimization method. The above optimal parameters are selected by minimizing objective function. Real ice condition time series at Bohai are taken to validate the new method. The prediction results indicate that the new method can choose the above optimal parameters in prediction process. Compared with improved genetic algorithm threshold autoregressive prediction model (IGATARPM) and standard genetic algorithm threshold autoregressive prediction model (SGATARPM), DNAOTARPM has higher precision and faster convergence speed for predicting nonlinear ice condition time series.

A refined rank set pair analysis model based on wavelet analysis for predicting temperature series

Purpose – The purpose of this paper is to accurately simulate and predict the daily extreme temperature in Beijing Reservoir and the monthly extreme temperature in Tianjin Reservoir using wavelet refined rank set pair analysis (WRRSPA). Design/methodology/approach – The new method, called WRRSPA, which combines wavelet analysis and refined rank set pair analysis (RRSPA), was proposed for use in this study because of the non-linear and multi-time scale characteristics of the temperature series. The model includes the advantages of the multi-resolution feature of wavelet analysis and the non-parametric data-driven prediction from refined rank set air analysis. Findings – Based on the daily extreme temperature of Beijing Reservoir, the predictions of the last 18 days reveal that WRRSPA is more appropriate because the percentage of the relative errors that are smaller than 10 percent increased from 78 percent by Back Propagation (BP) and 78 percent by RRSPA to 100 percent by WRRSPA in Beijing Reservoir. In ad...

Integrated assessment of water resources vulnerability under climate change in Haihe River Basin

Purpose – The purpose of this paper is to assess the water resources vulnerability (WRV) rationally in Haihe River Basin (HRB) using set pair analysis (SPA) theory. Design/methodology/approach – An improved intelligent set pair analysis (IISPA) model is established, in which intelligent SPA theory is introduced and the weights are determined by use of the maximum entropy principle and the improved analytic hierarchy process method. The index systems and criteria of WRV assessment in terms of water cycle, socio-economy, and ecological environment are established based on the analysis of sensibility and adaptability. Findings – The authors apply IISPA to the WRV assessment of seven administrative divisions in HRB. Results show IISPA can fully take advantage of certain and uncertain information compared with fuzzy assessment and topsis assessment models. For present situation, Shanxi, Shandong, Tianjing, Inner Mongolia, Hebei are higher, Henan and Beijing are the middle vulnerability. But Henan will become w...

Refined gray-encoded evolution algorithm for parameter optimization in convection-diffusion equations

Purpose – The purpose of this paper is to reduce the computational burden and improve the precision of the parameter optimization in the convection-diffusion equation, a new algorithm, the refined gray-encoded evolution algorithm (RGEA), is proposed. Design/methodology/approach – In the new algorithm, the differential evolution algorithm (DEA) is introduced to refine the solutions and to improve the search efficiency in the evolution process; the rapid cycle operation is also introduced to accelerate the convergence rate. The authors apply this algorithm to parameter optimization in convection-diffusion equations. Findings – Two cases for parameter optimization in convection-diffusion equations are studied by using the new algorithm. The results indicate that the sum of absolute errors by the RGEA decreases from 74.14 to 99.29 percent and from 99.32 to 99.98 percent, respectively, compared to those by the gray-encoded genetic algorithm (GGA) and the DEA. And the RGEA has a faster convergent speed than doe...

Rainfall-runoff simulation using improved evolution algorithm based on pattern search

Rainfall-runoff simulation is very important in practical environmental planning. To improve the computational accuracy for rainfall-runoff simulation, an improved evolution algorithm based on pattern search (IEAPS) is proposed, in which initial population are generated by chaos mapping and searching range is automatically renewed with the excellent individuals by pattern search. Its efficiency is verified by application of rainfall-runoff simulation for five rainfall events. Compared with standard binary-encoded genetic algorithm (SGA), pattern search algorithm (PSA), IEAPS has higher precision and rapider convergent speed. It is good for the global optimization in the practical rainfall-runoff simulation.