Relevant bibliographies by topics / Stochastic disaggregation

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Stochastic disaggregation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Stochastic disaggregation"

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Krzysztofowicz, Roman, and Thomas A. Pomroy. "Disaggregative Invariance of Daily Precipitation." Journal of Applied Meteorology 36, no. 6 (June 1, 1997): 721–34. http://dx.doi.org/10.1175/1520-0450-36.6.721.

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Abstract Disaggregative invariance refers to stochastic independence between the total precipitation amount and its temporal disaggregation. This property is investigated herein for areal average and point precipitation amounts accumulated over a 24-h period and disaggregated into four 6-h subperiods. Statistical analyses of precipitation records from 1948 to 1993 offer convincing empirical evidence against the disaggregative invariance and in favor of the conditional disaggregative invariance, which arises when the total amount and its temporal disaggregation are conditioned on the timing of precipitation within the diurnal cycle. The property of conditional disaggregative invariance allows the modeler or the forecaster to decompose the problem of quantitative precipitation forecasting into three tasks: (i) forecasting the precipitation timing; (ii) forecasting the total amount, conditional on timing; and (iii) forecasting the temporal disaggregation, conditional on timing. Tasks (ii) and (iii) can be performed independently of one another, and this offers a formidable advantage for applications.
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Lin, Gwo-Fong, and Fong-Chung Lee. "Multistage disaggregation processes in stochastic hydrology." Water Resources Management 6, no. 2 (1992): 101–15. http://dx.doi.org/10.1007/bf00872206.

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Sivakumar, B., W. W. Wallender, C. E. Puente, and M. N. Islam. "Streamflow disaggregation: a nonlinear deterministic approach." Nonlinear Processes in Geophysics 11, no. 3 (September 8, 2004): 383–92. http://dx.doi.org/10.5194/npg-11-383-2004.

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Abstract. This study introduces a nonlinear deterministic approach for streamflow disaggregation. According to this approach, the streamflow transformation process from one scale to another is treated as a nonlinear deterministic process, rather than a stochastic process as generally assumed. The approach follows two important steps: (1) reconstruction of the scalar (streamflow) series in a multi-dimensional phase-space for representing the transformation dynamics; and (2) use of a local approximation (nearest neighbor) method for disaggregation. The approach is employed for streamflow disaggregation in the Mississippi River basin, USA. Data of successively doubled resolutions between daily and 16 days (i.e. daily, 2-day, 4-day, 8-day, and 16-day) are studied, and disaggregations are attempted only between successive resolutions (i.e. 2-day to daily, 4-day to 2-day, 8-day to 4-day, and 16-day to 8-day). Comparisons between the disaggregated values and the actual values reveal excellent agreements for all the cases studied, indicating the suitability of the approach for streamflow disaggregation. A further insight into the results reveals that the best results are, in general, achieved for low embedding dimensions (2 or 3) and small number of neighbors (less than 50), suggesting possible presence of nonlinear determinism in the underlying transformation process. A decrease in accuracy with increasing disaggregation scale is also observed, a possible implication of the existence of a scaling regime in streamflow.
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Gao, Xiaogang, and Soroosh Sorooshian. "A Stochastic Precipitation Disaggregation Scheme for GCM Applications." Journal of Climate 7, no. 2 (February 1994): 238–47. http://dx.doi.org/10.1175/1520-0442(1994)007<0238:aspdsf>2.0.co;2.

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Buchholz, Peter. "An Aggregation/Disaggregation Algorithm for Stochastic Automata Networks." Probability in the Engineering and Informational Sciences 11, no. 2 (April 1997): 229–53. http://dx.doi.org/10.1017/s0269964800004782.

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Stochastic automata networks (SANs) have recently received much attention in the literature as a means to analyze complex Markov chains in an efficient way. The main advantage of SANs over most other paradigms is that they allow a very compact description of the generator matrix by means of much smaller matrices for single automata. This representation can be exploited in different iterative techniques to compute the stationary solution. However, the set of applicable solution methods for SANs is restricted, because a solution method has to respect the specific representation of the generator matrix to exploit the compact representation. In particular, aggregation/disaggregation (a/d) methods cannot be applied in their usual realization for SANs without losing the possibility to exploit the compact representation of the generator matrix.In this paper, a new a/d algorithm for SANs is introduced. The algorithm differs significantly from standard a/d methods because the parts to be aggregated are defined in a completely different way, exploiting the structure of the generator matrix of a SAN. Aggregation is performed with respect to single automata or sets of automata, which are the basic parts generating a SAN. It is shown that the new algorithm is efficient even if the automata are not loosely coupled.
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Bo, Zhiquan, Shafiqul Islam, and E. A. B. Eltahir. "Aggregation-disaggregation properties of a stochastic rainfall model." Water Resources Research 30, no. 12 (December 1994): 3423–35. http://dx.doi.org/10.1029/94wr02026.

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Gagnon, P., and A. N. Rousseau. "Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed." Hydrology and Earth System Sciences 18, no. 5 (May 9, 2014): 1695–704. http://dx.doi.org/10.5194/hess-18-1695-2014.

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Abstract. Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy – CAPE), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However, it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation illustrates that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.
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Gagnon, P., and A. N. Rousseau. "Stochastic spatial disaggregation of extreme precipitation to validate a Regional Climate Model and to evaluate climate change impacts over a small watershed." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 26, 2013): 8167–95. http://dx.doi.org/10.5194/hessd-10-8167-2013.

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Abstract. Regional Climate Models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation: (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy (CAPE)), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation permits to determine that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.
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Wright, S. E. "Primal-Dual Aggregation and Disaggregation for Stochastic Linear Programs." Mathematics of Operations Research 19, no. 4 (November 1994): 893–908. http://dx.doi.org/10.1287/moor.19.4.893.

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Schleiss, Marc, and Alexis Berne. "Stochastic Space–Time Disaggregation of Rainfall into DSD fields." Journal of Hydrometeorology 13, no. 6 (December 1, 2012): 1954–69. http://dx.doi.org/10.1175/jhm-d-12-013.1.

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Abstract A stochastic method to disaggregate rain rate fields into drop size distribution (DSD) fields is proposed. It is based on a previously presented DSD simulator that has been modified to take into account prescribed block-averaged rain rate values at a coarser scale. The integral quantity used to drive the disaggregation process can be the rain rate, the radar reflectivity, or any variable directly related to the DSD. The proposed method is illustrated and qualitatively evaluated using radar rain rate data provided by MeteoSwiss for two rain events of very contrasted type (stratiform versus convective). The evaluation shows that both types of rainfall are correctly disaggregated, although the general agreement in terms of rain rate distributions, intermittency, and space–time structures is much better for the stratiform case. Possible extensions and generalizations of the technique (e.g., using radar reflectivities at two different frequencies or polarizations to drive the disaggregation process) are discussed at the end of the paper.
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Dissertations / Theses on the topic "Stochastic disaggregation"

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Mahbub, S. M. Parvez Bin, and s. mahbub@qut edu au. "Stochastic Disaggregation of Daily Rainfall for Fine Timescale Design Storms." Central Queensland University. Centre for Railway Engineering, 2008. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20080813.151345.

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Rainfall data are usually gathered at daily timescales due to the availability of daily rain-gauges throughout the world. However, rainfall data at fine timescale are required for certain hydrologic modellings such as crop simulation modelling, erosion modelling etc. Limited availability of such data leads to the option of daily rainfall disaggregation. This research investigates the use of a stochastic rainfall disaggregation model on a regional basis to disaggregate daily rainfall into any desired fine timescale in the State of Queensland, Australia. With the incorporation of seasonality into the variance relationship and capping of the fine timescale maximum intensities, the model was found to be a useful tool for disaggregating daily rainfall in the regions of Queensland. The degree of model complexity in terms of binary chain parameter calibration was also reduced by using only three parameters for Queensland. The resulting rainfall Intensity-Frequency-Duration (IFD) curves better predicted the intensities at fine timescale durations compared with the existing Australian Rainfall and Runoff (ARR) approach. The model has also been linked to the SILO Data Drill synthetic data to disaggregate daily rainfall at sites where limited or no fine timescale observed data are available. This research has analysed the fine timescale rainfall properties at various sites in Queensland and established sufficient confidence in using the model for Queensland.
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Al-Tamimi, Rami Salhab. "Continuous time disaggregation in hierarchical production planning." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001819.

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Samadzad, Mahdi. "Space disaggregation in models of route and mode choice : method and application to the Paris area." Thesis, Paris Est, 2013. http://www.theses.fr/2013PEST1058/document.

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La repr?sentation spatiale de l?aire de mod?lisation dans les mod?les de la demande de transports a peu chang? au cours des derni?res d?cennies. A cet ?gard, l??tat-de l?art repose encore largement sur le syst?me de centro?de-connecteur qui est utilis?e dans les mod?les classiques. Elle est une approche agr?g?e qui ignore la variabilit? physique li?e ? la dispersion des lieux d?sagr?g?s de r?sidence et d?activit? dans l?espace local. En cons?quence, le pouvoir explicatif des mod?les quant aux comportements de choix d?itin?raire et de mode demeure limit? ? l??chelle locale : Par exemple, la localisation d?sagr?g?e influence sur le choix entre une autoroute dont l??changeur est ?loign?, et un autre itin?raire non-autoroutier. Egalement, le rabattement terminal influence sur le partage modal auto vs. transports en commun. Nous pr?sentons une approche d?sagr?g?e pour la repr?sentation spatiale. Dans un d?coupage zonal, l?espace ? l?int?rieur d?une zone est repr?sent? de mani?re d?sagr?g?e stochastique. Pour chaque zone, les points d?ancrage sont d?finis relative aux n?uds du r?seau qui peuvent ?tre utilis?s pour acc?der au r?seau. Un itin?raire entre une paire de zones est ensuite consid?r? comme une chaine, compos?e de deux trajets terminaux, correspondants aux sections intrazonales de l?itin?raire, et d?un trajet principal correspondant ? la section entre deux points d?ancrage. En cons?quence, le mod?le de choix d?itin?raire est transform? ? un mod?le de choix conjoint d?une paire de point d?ancrage. Le vecteur des temps al?atoires terminaux est Normal Multidimensionnel donnant lieu ? un mod?le Probit de choix conjoint de points d?ancrage.Pour ?tendre au cadre multimodal, un mode collectif composite est d?fini comme une chaine compos?e des trois trajets modaux d?acc?s, principal, et de sortie, et les stations sont consid?r?es comme les points d?ancrage, connectant les trajets de rabattement au trajet principal. Un mod?le Logit Multinomial de choix de mode est estim? ? partir de l?Enqu?te Globale de Transport de 2001 pour le mode auto et le faisceau des modes collectifs composites, et est combin? avec les deux mod?les Probit correspondants au choix des stations
Spatial representation of modeling area in travel demand models has changed little over the course of last several decades. In this regard, the state-of-the-art still widely relies on the same centroid-connector system that has been used in classic models. In this approach continuum bidimensional space is lumped on centroids. It is an aggregate approach which ignores the physical variability linked to the scatteredness of disaggregate residence- and activity-places over the local space. Consequently the modeling performance in explaining route and mode choice behavior degrades at local scales: In route choice, disaggregate location influences the propensity between a distant interchange to a highway, or a nearby road. In mode choice, feeder service to public transportations influences the auto vs. transit modal share. We propose a disaggregate approach for spatial representation. Based on a zoning system, a stochastic disaggregate representation is used to characterize the space within a traffic analysis zone. For each zone, anchor-points are defined as the network nodes that are used for accessing to the network from within the local space. An itinerary between a pair of zones is then considered as a chain of legs composed of two terminal legs, corresponding to the intrazonal route sections, and one main leg between two anchor points. The route choice problem is transformed to a joint choice of a pair of anchor points. The vector of random terminal travel times is Multivariate Normal resulting in a Multinomial Probit model of choice of a pair of anchor points. To extend to the multimodal context, a transit composite mode is defined as a chain of access, main, and egress modal legs, and transit platforms are considered as anchor points connecting the feeder legs to the main line-haul leg. A Multinomial Logit mode choice model is estimated based on the 2001 Paris Household Travel Survey for the auto mode and the composite transit modes. It is joined with the two Multinomial Probit models corresponding to the choice of anchor points. The result is a joint model of mode and station choice with a disaggregate representation of the local space
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Zhang, Jingwei. "Numerical Methods for the Chemical Master Equation." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/30018.

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The chemical master equation, formulated on the Markov assumption of underlying chemical kinetics, offers an accurate stochastic description of general chemical reaction systems on the mesoscopic scale. The chemical master equation is especially useful when formulating mathematical models of gene regulatory networks and protein-protein interaction networks, where the numbers of molecules of most species are around tens or hundreds. However, solving the master equation directly suffers from the so called "curse of dimensionality" issue. This thesis first tries to study the numerical properties of the master equation using existing numerical methods and parallel machines. Next, approximation algorithms, namely the adaptive aggregation method and the radial basis function collocation method, are proposed as new paths to resolve the "curse of dimensionality". Several numerical results are presented to illustrate the promises and potential problems of these new algorithms. Comparisons with other numerical methods like Monte Carlo methods are also included. Development and analysis of the linear Shepard algorithm and its variants, all of which could be used for high dimensional scattered data interpolation problems, are also included here, as a candidate to help solve the master equation by building surrogate models in high dimensions.
Ph. D.
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Book chapters on the topic "Stochastic disaggregation"

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Lane, William L. "Aggregation and Disaggregation Modelling." In Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization, 97–116. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1697-8_5.

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Reyman, Grzegorz, and Jan van der Wal. "Aggregation — Disaggregation Algorithms for Discrete Stochastic Systems." In Operations Research Proceedings, 515–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73778-7_136.

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Marco, Juan B. "The Segura River Basin Model. Disaggregation in a Semiarid Environment." In Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization, 413–23. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1697-8_25.

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POSCHINGER, A., R. KATES, and H. KELLER. "Coupling of Concurrent Macroscopic and Microscopic Traffic Flow Models Using Hybrid Stochastic and Deterministic Disaggregation." In Transportation and Traffic Theory in the 21st Century, 583–605. Elsevier, 2002. http://dx.doi.org/10.1016/b978-008043926-6/50031-2.

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Andreas, Poschinger, AG Siemens, Kates Ronald, and Keller Hartmut. "Coupling of Concurrent Macroscopic and Microscopic Traffic Flow Models using Hybrid Stochastic and Deterministic Disaggregation." In Transportation and Traffic Theory in the 21st Century, 583–605. Emerald Group Publishing Limited, 2002. http://dx.doi.org/10.1108/9780585474601-029.

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Conference papers on the topic "Stochastic disaggregation"

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Heracleous, Panikos, Pongtep Angkititraku, and Kazuya Takeda. "Stochastic modeling and disaggregation of energy-consumption behavior." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855215.

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di Filippo, Rocco, Giuseppe Abbiati, Osman Sayginer, Patrick Covi, Oreste S. Bursi, and Fabrizio Paolacci. "Numerical Surrogate Model of a Coupled Tank-Piping System for Seismic Fragility Analysis With Synthetic Ground Motions." In ASME 2019 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/pvp2019-93685.

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Abstract Seismic risk evaluation of coupled systems of industrial plants often needs the implementation of complex finite element models to consider their multicomponent nature. These models typically rely on significant computational resources. Moreover, the relationships between seismic action, system response and relevant damage levels are often characterized by a high level of nonlinearity, thus requiring a solid background of experimental data. Furthermore, fragility analyses depend on the adoption of a significant number of seismic waveforms generally not available when the analysis is site-specific. To propose a methodology able to manage these issues, we present a possible approach for a seismic reliability analysis of a coupled tank-piping system. The novelty of this approach lies in the adoption of artificial accelerograms, FE models and experimental hybrid simulations to evaluate a surrogate meta-model of our system. First, to obtain the necessary input for a stochastic ground motion model able to generate synthetic ground motions, a disaggregation analysis of the seismic hazard is performed. Hereafter, we reduce the space of parameters of the stochastic ground motion model by means of a global sensitivity analysis upon the seismic response of our system. Hence, we generate a large set of synthetic ground motions and select, among them, a few signals for experimental hybrid simulations. In detail, the hybrid simulator is composed by a numerical substructure to predict the sliding response of a steel tank, and a physical substructure made of a realistic piping network. Furthermore, we use these experimental results to calibrate a refined ANSYS FEM. More precisely, we focus on tensile hoop strains in elbow pipes as a leading cause for leakage, monitoring them with strain gauges. Thus, we present the procedure to evaluate a numerical Kriging meta-model of the coupled system based on both experimental and finite element model results. This model will be adopted in a future development to carry out a seismic fragility analysis.
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