Emergency recovery plan selection (ERPS) is critical for managing post-disaster recovery and ensuring long-term societal stability. However, current multi-attribute decision-making (MADM) research on ...ERPS is limited and lacks consideration of Pareto-optimal solutions and expert consensus resulting from multi-stakeholder involvement. Therefore, this study proposes a hybrid Dynamic Grey Relation Analysis and Partial Ordinal Priority Approach (DGRA-POPA) model for ERPS. The proposed approach employs stable and easily accessible ranking data as inputs. DGRA is first utilized to extract consistency in attribute preferences among experts and serves as the basis for determining expert rankings. Considering expert consensus and information distribution, preference modification coefficients are derived and embedded into POPA. Through decision-weight optimization, partial-order cumulative transformation, and dominance structure generation, the weights for experts, attributes, and alternatives are determined along with a Hasse diagram. This diagram offers Pareto-optimal and suboptimal alternatives and alternative clustering information. The proposed approach is demonstrated using the ERPS after the Manchester Stadium attack. Sensitivity and comparative analyses with ten different MADM methods validate the effectiveness. Overall, the proposed approach enhances ERPS transparency, stability, and robustness by identifying Pareto-optimal alternatives while considering expert consensus and information distribution.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and ...identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The ultra-rapid satellite clock product based on the satellite clock batch estimation is commonly used for high-precision and reliable precise point positioning (PPP) services. In order to clarify ...the effect of different ranging errors on the satellite clock batch estimation accuracy, the source of the satellite clock bias induced by the batch observation model is classified into the initial clock bias (ICB) and time-dependent bias (TDB). In addition to the effect of the ICB and TDB, the analytic relationship between the observation redundancy and the satellite clock batch estimation accuracy are derived and verified. The suitable number of stations is suggested to be 40 for the satellite clock batch estimation to achieve the counterbalance between the efficiency and saturable accuracy. For the PPP based on the batch-estimated satellite clock, the impacts of the ICB and TDB on PPP are clarified. The satellite clock batch estimation and PPP experiments are carried out to investigate the impacts of the ICB and TDB on the satellite clock batch estimation accuracy and the PPP performance. The ICB causes a significant bias for the batch-estimated satellite clock. The TDB is impacted by the assimilation ability of the batch-estimated satellite clock to the satellite orbit error. The convergence time and the positioning accuracy after the convergence of PPP are primarily affected by the ICB and TDB, respectively.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Since the initial impoundment and commissioning of the Three Gorges Reservoir in June 2003, seismic activity surrounding the reservoir region has undergone substantial changes. Leveraging geological ...and hydrogeological data from the Three Gorges Reservoir area, this study statistically analyzes the historical water level and earthquake catalog within the reservoir. By examining the correlation between reservoir water levels and earthquake frequency, the relationship between seismicity along the Xiannvshan fault and water level is analyzed. Additionally, the ArcGIS software is employed to evaluate the spatial pattern of earthquake epicenters during the filling of the Three Gorges Reservoir, with the goal of elucidating the impact of water impoundment at the Three Gorges on the activity along the Xiannvshan fault. The results demonstrate the following. (1) There is a complex process of “continuous loading, permeation and saturation, rebound and rebalancing” in the crust of the reservoir head area during the impoundment of the Three Gorges Reservoir area, and the activity of the Xiannvshan fault is closely related to the reservoir water level. (2) At the 135 m impoundment stage, Xiannvshan fault activity is mainly affected by reservoir water level and is positively correlated with reservoir water level. At the 156 m impoundment stage, reservoir water load is still the main influencing factor of Xiannvshan fault activity, but the permeability of reservoir water is enhanced in this stage. (3) The earthquake epicenters near the northern section of the Xiannvshan fault are clustered during the 175 m experimental impoundment stage. During the continuous loading stage, the reservoir water load is still the main control factor of the Xiannvshan fault, and the seismic activity is significantly enhanced. From November 2010 to November 2013, during the permeation and saturation stage, the dominant factor of Xiannvshan fault activity changed from reservoir water load to reservoir water infiltration along the Xiannvshan fault. The period from 2013.11 to 2014.5 was a vertical rebound stage, and the infiltration effect of reservoir water had a more significant impact on Xiannvshan fault activities.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Knowledge of the signal-in-space (SIS) anomaly probability is important for the integrity monitoring of satellite navigation. An efficient SIS anomaly detection method is indispensable for ...characterizing the probability of SIS anomalies with sufficient confidence. The traditional GPS anomaly detection method based on the zero-mean Gaussian distribution assumption of the instantaneous signal-in-space user range error (IURE) is not suitable for the emerging BeiDou navigation satellite system (BDS) because of the nonzero-mean, asymmetric distribution of the BDS IURE. By deliberately extracting the time series trend terms of the satellite orbit and clock errors, an SIS anomaly detection method with the worst user location protection principle is proposed based on 6 years of BDS data from March 2013 to March 2019. The detection results have shown that the probability of single-satellite SIS anomalies is at the 10
−3
level and the probability of multiple-satellite SIS concurrent anomalies is at the 10
−4
level. Meanwhile, the operational service performance provided by the BDS gradually improves over time.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the ...Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.
The COVID-19 pandemic, characterized by high uncertainty and difficulty in prevention and control, has caused significant disasters in human society. In this situation, emergency management of ...pandemic prevention and control is essential to reduce the pandemic’s devastation and rapidly restore economic and social stability. Few studies have focused on a scenario analysis of the entire emergency response process. To fill this research gap, this paper applies a cross impact analysis (CIA) and interpretive structural modeling (ISM) approach to analyze emergency scenarios and evaluate the effectiveness of emergency management during the COVID-19 crisis for outbreak prevention and control. First, the model extracts the critical events for COVID-19 epidemic prevention and control, including source, process, and resultant events. Subsequently, we generated different emergency management scenarios according to different impact levels and conducted scenario deduction and analysis. A CIA-ISM based scenario modeling approach is applied to COVID-19 emergency management in Nanjing city, China, and the results of the scenario projection are compared with actual situations to prove the validity of the approach. The results show that CIA-ISM based scenario modeling can realize critical event identification, scenario generation, and evolutionary scenario deduction in epidemic prevention and control. This method effectively handles the complexity and uncertainty of epidemic prevention and control and provides insights that can be utilized by emergency managers to achieve effective epidemic prevention and control.
Abstract
The
a priori
fault probability of the real-time precise satellite orbit and clock correction products is the critical parameter for integrity monitoring of precise point positioning (PPP). ...The traditional fault probability evaluation methods use the worst-case instantaneous user ranging error (IURE) as the conservative test statistic. However, the systematic biases of IURE contained in the worst-case IURE barely affect the PPP accuracy, which will undermine the statistical distribution of test statistic and reduce the sensitivity of fault detection. The fault probability will be estimated over-conservatively for the traditional methods. By clarifying the sources of the systematic biases, a new test statistic is constructed by deliberately removing the systematic biases of IURE originated from satellite orbit and clock errors. One-year Global Positioning System correction products evaluation results have demonstrated that the constructed test statistic follows the Gaussian distribution with the decreased uncertainty and the improved fault detection sensitivity. The real-world data experiments have shown that the
a priori
probabilities of the satellite fault and the constellation fault are at the order of 10
−4
and 10
−5
levels, respectively.
The Accelerator Driven Advanced Nuclear Energy System (ADANES) is currently undergoing research and development (R&D), presenting challenges in cost estimation due to significant uncertainties. ...Traditional nuclear power cost assessment methods, tailored for mature technologies, lack relevance for advanced systems like ADANES. To address this gap, our study proposes a unique cost analysis approach, dividing ADANES into two stages: the experimental research and development (ERD) stage and the industrial demonstration (ID) stage. Specificlly, in the ERD stage, this study employs a Logistic function-based evaluation method that considers factors such as construction period extension, the proportion of fixed costs, and the upper limit of cost estimated by experts to address potential cost overruns. For the ID stage, this study utilizes a stochastic differential equation (SDE) to account for uncertainties. Monte Carlo simulation is employed to analyze the impact of parameter changes, including construction period extension and acceptable upper limits of cost and duration. Results reveal a substantial increase in expected cost during the ERD stage, ranging from 100% to 140% of the original budget when extending the experimental research duration by 10% to 50%. The ID stage demonstrates an even more significant impact, with a 50% construction period extension resulting in an expected cost of 182% of the original budget. The study suggests that judiciously extending acceptable cost and duration caps can enhance the project's success rate. This innovative cost analysis approach provides valuable insights for navigating the uncertainties associated with ADANES development.
•A cost analysis approach is introduced for the disruptive ADANES nuclear energy system.•It employs a logistic function-based evaluation method and stochastic differential equation to account for uncertainties.•A case study validates the method, revealing the numerical relationship between time extension and cost overrun of ADANES.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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