Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources. Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task. To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series. Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction. CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity. Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose. Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP. The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively. However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively. The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction. The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff. Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources.
A strategic approach to behavioral change communication streamlines communication processes of a health institution in a crisis setting like COVID-19 pandemic. In such a setting, it is important to focus communication efforts to reach the different audience groups and ensure common understanding and willingness to act by all the groups in order to achieve the institution’s mission of curbing the pandemic. This study contributes to these efforts by examining the mediating effect of interaction resonance in the relationship between information adequacy and strategic behavioral change communication. The study adopted a cross sectional survey design that involved collecting quantitative data from 223 health organizations of Uganda’s health sector in the different regions of the country. In order to test the study hypotheses, the study used Structural Equation Modeling of AMOS and the bootstrapping approach to test the mediating role of interaction resonance. The results revealed that interaction resonance fully mediates in the relationship between information adequacy and strategic behavioral change communication. This implied that having adequate information per say, does not cause behavioral change among the intended message recipients but requires a communication system that enables high quality interactions.
Purpose – The purpose of this paper is to examine the human dimension of project management by establishing the extent to which social networks influence the commitment of project stakeholders. Up to date, project managers still identify inadequate stakeholder commitment to project undertakings as a key antecedent of project failure and so efforts aimed at addressing this challenge are highly valued. The paper therefore explores the use of social networks as one of the possible strategies to enhance project-stakeholder commitment. Design/methodology/approach – The paper adopts a quantitative, cross-sectional study design. Based on responses from 172 project stakeholders who took part in a sample of 92 citizenship projects conducted by major commercial banks in Uganda, hierarchical regression was used to indicate what happens to a model as different predictor variables are introduced The use of specific type of projects minimizes bias in results due to the unique nature of specific projects hence enhances reliability of results. Findings – The results from statistical analysis reveal that social network elements (network transitivity and network degree) are significant predictors of project stakeholder commitment. The results also suggest that network transitivity is a better significant predictor of project-stakeholder commitment than network degree Practical implications – Project-stakeholder commitment has been widely studied in relation to project performance and the study makes a number of contributions to the theory and study of projects. First and foremost, the paper studied project social networks and project-stakeholder commitment in citizenship projects in commercial banks in Uganda which is a developing country. The study therefore contributes to an understanding of project social networks and project-stakeholder commitment in citizenship projects of commercial banks in a developing country. The implication of the findings is that it provides a different view point of understanding the aspects that affect project commitment. A lot of focus has been placed on improving project performance in Uganda, but none has specifically focussed on building project-stakeholder commitment through the use of project social networks. Originality/value – Earlier attempts to investigate the impact of social networks on commitment in projects did not study commitment among individuals. Also, no previous empirical study in less developed countries has given special attention to the effect of social networks on project-stakeholder commitment especially in the domain of citizenship projects which have gained a lot of momentum around the globe. The study results indicate that getting concerned with the nature of social networks the project creates and the means it uses to maintain such networks has implications for project-stakeholder commitment.
The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDAN-GRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.
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