Value of security: modeling time-dependent phenomena and weather conditions

Ríos, Mario Alberto Gaviria; Kirschen, Daniel S.; Jayaweera, Dilan; Nedic, Dusko P.; Allan, R.N.

doi:10.1109/tpwrs.2002.800872

Cited by 183 publications

(17 citation statements)

References 12 publications

(19 reference statements)

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“…These studies could be simulated by different models, such as the water and soil equilibrium model [28,29], Empirical model [30], conceptual model [31] and complex distribution model [32,33]. In addition, Changnon et al [34] have studied the relationship between monthly precipitation (P) and the shallow groundwater level (GW) in 20 wells scattered across the American state of Illinois using autoregressive integrated moving average (ARIMA) modeling.…”

Section: Linkages Between Climate Variability and Water Resourcesmentioning

confidence: 99%

The Influence of Climate Variability Effects on Groundwater Time Series in the Lower Central Plains of Thailand

Taweesin

Seeboonruang

Saraphirom

2018

Water

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Abstract:This research studies the relationship between the climate index and the groundwater level of the lower Chao Phraya basin, in order to forecast the groundwater level in the studied area by using Autoregressive Integrated Moving Average with Explanatory (ARIMAX). The combination of 6 climate indices-Dipole Mode Index, Indian Summer Monsoon Index, Multivariate ENSO Index, Sea Surface Temperature NINO4, Southern Oscillation Index and the Western North Pacific Monsoon Index-were used, along with the groundwater level data from 14 stations during the period 1980-2011 to develop the forecast model and verify it with the data of 2012.The first step was correlation of the ARIMA model with Autocorrelation Function and Partial Autocorrelation Function. The possible model was then selected using BIC statistics. Diagnostic Checking was done to consider the white noise characteristic of estimated residuals by using the statistics of Box and Ljung (Q-statistic). If the selected models were found to be proper, then the Granger Causality Test of the leading parameters or the climate index would be performed as the next step. The results show that there is a relationship between the groundwater level and the climate index. The model could be used to forecast effectively the average RMSE value at 0.6. The last step was to develop the MODFLOW for a conceptual model and synthesize groundwater levels in the study area, which covers around 43,000 km 2 and has 8 layers of groundwater, with Bangkok clay on the top. All other boundary values were set to be steady. The calibration was done using the data of 325 observed wells. The normalized RMS value was 9.705%. The results were verified by the data using ARIMAX over the same time periods. To conclude, the simulated results of the monthly groundwater level in 2012 of the wells have a confidence interval of around 95%, which is near the result from the ARIMAX model. The advantages of the ARIMAX model include high accuracy, no requirement for a large amount of data and inexpensive implementation. It is one of the effective tools for the groundwater prediction.

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Section: Linkages Between Climate Variability and Water Resourcesmentioning

confidence: 99%

The Influence of Climate Variability Effects on Groundwater Time Series in the Lower Central Plains of Thailand

Taweesin

Seeboonruang

Saraphirom

2018

Water

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“…However, since all of the considered meteorological variables contribute to describing the higher-level phenomenon, which is the weather, this loss of structure is in fact an advantage in terms of minimizing data redundancy. The resulting factors should account for simple weather conditions such as normal or adverse—as defined similarly in the context of failure rates in power transmission networks [37]. …”

Section: Methodsmentioning

confidence: 99%

Ubiquitous Geo-Sensing for Context-Aware Analysis: Exploring Relationships between Environmental and Human Dynamics

Sagl

Blaschke

Beinat

et al. 2012

Sensors

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Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the ‘global’ adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods using the Maximal Information Coefficient (MIC). The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges.

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“…Though several risk-based approaches to security assessment have been proposed by researchers in the last few years [3][4][5][6][7][8][9][10], the risk concept has been introduced only very recently by a few operational standards to deal with extreme events affecting an uncertain PS [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Dynamic Security Assessment for Power System Operation and Operational Planning

et al. 2017

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Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

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Value of security: modeling time-dependent phenomena and weather conditions

Cited by 183 publications

References 12 publications

The Influence of Climate Variability Effects on Groundwater Time Series in the Lower Central Plains of Thailand

The Influence of Climate Variability Effects on Groundwater Time Series in the Lower Central Plains of Thailand

Ubiquitous Geo-Sensing for Context-Aware Analysis: Exploring Relationships between Environmental and Human Dynamics

Risk-Based Dynamic Security Assessment for Power System Operation and Operational Planning

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