Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid- and long-term rainfall forecasting.
This work reports a gold nanoelectrode ensembles (Au-NEE) platform taken as a disposable electrogenerated chemiluminescence (ECL) platform with immunomagnetic microbeads for ECL immunoassays for the first time. The peak-shaped voltammograms were obtained at the Au-NEE, attributed to the total diffusional overlap. The ECL intensity at Au-NEE was 12.9 folds in the Ru(bpy) 3 2+ -tri-n-propylamine (TPA) ECL system and 19.6 folds in the luminol-H 2 O 2 system, compared with that at the Au macroelectrode using the normalized active area of the electrodes, mainly attributed to the diffusion overlap of the Au-NEE and the edge effect of the individual gold nanodisks of the Au-NEE. The ECL immunoassay on the Au-NEE platform with magnetic microbeads for the determination of cancer biomarkers was developed. Carbohydrate antigen 19-9 (CA 19-9) was chosen as a model analyte while CA 19-9 antibody on the magnetic microbeads was taken as the capture probe, and ruthenium complex-labeled CA 19-9 antibody was used as the signal probe. A "sandwich" bioconjugates on the magnetic beads were transferred onto the ECL platform, and then the ECL measurements were performed in TPA solution. The developed method showed that the ECL peak intensity was directly in proportion to the concentration of CA 19-9 in the range from 0.5 to 20 U/mL with a limit of detection of 0.4 U/mL. This work demonstrates that the Au-NEE can be employed as a useful disposable ECL platform with the merits of cheapness, low nonspecific adsorption and practical application. The proposed approach will open a new avenue in the point-of-care test for the determination of protein biomarkers.
Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.
The dynamics of cross-flow tubes were studied in consideration of initial axial load and distributed impacting constraints, modeled as cubic and trilinear spring constraints. The tubes were modeled as Euler–Bernoulli beams and supported at both ends, including the simply supported tube and clamped-clamped tube. The analytical model involves a time-delayed displacement term induced by the cross flow based on the quasi-steady theory. For simplicity, a single flexible supported beam in a rigid square array of cylinders was studied by using the damping-controlled mechanism. The mean extension of the tube was considered, and thus, it added another nonlinear term in the equation of motion. Results show that the tube loses stability by buckling and fluttering at various initial pressure loads and cross-flow velocities. An increase was observed for critical velocities and initial pressure loads. Chaotic oscillations were observed for the trilinear spring model. The distribution of the impacting forces was also calculated. Some of the fresh results obtained in the impact system are expected to be helpful in understanding and controlling the dynamic responses of fluid-conveying pipes.
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