Relevant bibliographies by topics / Stochastic disaggregation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  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 (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
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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 (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 disaggr
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Gao, Xiaogang, and Soroosh Sorooshian. "A Stochastic Precipitation Disaggregation Scheme for GCM Applications." Journal of Climate 7, no. 2 (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 (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
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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 (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 (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 disaggregatio
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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 (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 disaggregatio
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Wright, S. E. "Primal-Dual Aggregation and Disaggregation for Stochastic Linear Programs." Mathematics of Operations Research 19, no. 4 (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 (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 ve
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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 int
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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 loc
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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
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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. 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. 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. 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. 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. 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 s
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Journal articles
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