Low-dimensional change models for flood
Low Dimensional Manifold Distributional Semantic Models
... a low-dimensional manifold DSM consting of four steps: 1) identify the domains that correspond to the low-dimensional manifolds, 2) run the dimensionality reduction al- gorithm for each ...
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Learning Low-Dimensional Models for Heterogeneous Data
... PCA is known to generally perform well in the presence of low to moderate homoscedastic noise and in the presence of missing data [ 43 ]. When the noise is standard normal, PCA gives the maximum likelihood (ML) ...
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Generative Models for Low-Dimensional Video Representation and Reconstruction
... Abstract Finding compact representation of videos is an essential component in almost every problem related to video processing or understanding. In this paper, we propose a generative model to learn compact latent codes ...
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Intelligible Models: Recovering Low Dimensional Additive Structure For Machine Learning Models
... linear models. For high dimensional datasets, it is usually hard to model nonlinearities or higher order feature inter- actions [47] because these complex models can be easily ...bias models ...
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LOW-DIMENSIONAL MODELS FOR MISSING DATA IMPUTATION IN ROAD NETWORKS
... construct low-dimensional representation of large and diverse networks, in presence of missing historical and neighboring ...these low-dimensional models to reconstruct data profiles ...
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Comparison of one-dimensional and two-dimensional computer models of water flow over flood plains using the 1986 and 1994 floods on the Waihao River Flood Plain
... maximum flood levels pegged and surveyed for all the cross ...these flood levels took into account all the outflows from the breaches and ...the flood log and the times when overtopping was over ...a ...
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Comparison of one-dimensional and two-dimensional computer models of water flow over flood plains using the 1986 and 1994 floods on the Waihao River Flood Plain
... Notes: Underneath each photograph are the code, a description including the direction of the photograph, the time and the person who took the photograph who was generally a resident of the Waihao River Flood Plain ...
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Comparison of one-dimensional and two-dimensional computer models of water flow over flood plains using the 1986 and 1994 floods on the Waihao River Flood Plain
... Time 20.12 mins on tape, Flood debris mark on fence west of Willowbridge Rd just south of Bradshaw’ s Rd intersection.. (Apologies for tripod reflection).[r] ...
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Perceptual models of uncertainty for socio hydrological systems:a flood risk change example
... Sources of uncertainty in flood risk change analysis and their characteristics. Relative importance (last column) is left blank as these 420"[r] ...
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Change point estimation in high dimensional Markov random-field models
... graphical models, the proposed methodology can be applied more broadly for model-based change-point ...high-dimensional change-point estimation ...
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Change point estimation in high dimensional Markov random‐field models
... a change point are depicted in ...first-stage change point estimate. The vote corresponding to the change point occurred on January 17th, 1995, at the beginning of the tenure of the 104th ...This ...
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Dynamic Flood Inundation Forecast for the City of Kulmbach Using Offline Two-Dimensional Hydrodynamic Models
... hydrodynamic-based flood forecast focusing on sce- nario generation and database queries to select the appropriate flood inundation map in ...operational flood forecasting, discharges are forecast at ...
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Application of re/insurance models to estimate increases in flood risk due to climate change
... G-CAT®‘s flood event catalogue is defined by daily streamflow estimates, the 24-h IDF curves served as in- puts for adjusting G-CAT® to reflect future climate ...of flood events in the entire prob- ability ...
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Efficient Inferential Methods in Regression Models with Change Points or High Dimensional Covariates.
... CHAPTER 1 Introduction A principal task in modern statistics is to provide computationally and theoretically efficient solutions for non-standard regression set-ups. These problems find application in various disciplines ...
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Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
... custom models, relying on historical speed-density link relationships for that specific network ...simulation models. However, such models are not generic and cannot be translated from one network to ...
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The inevitability of change in Chinese and Indian automakers' low cost productive models
... relatively low levels of intellectual property protection in China, which allowed domestic companies to reverse-engineer foreign car ...Chinese low-cost suppliers than was and is practiced by foreign ...
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Contributions to High-dimensional Data Analysis using Factor Models and Low Rank Approximations.
... tical models and optimization techniques have been proposed and developed to address the high-dimensional issue of these ...high dimensional and complex real ...
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Climate change and flood frequency in the UK
... climate change impacts is being informed by improved modelling capabilities and climate change ...rainfall models and fine resolution regional climate model data are now available to drive the ...
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Climate change impact on flood hazard
... changes models were used to define the impact of climate ...hydrological models to analyse the impact in which the uncertainty analysis of maximum discharge prediction is also ...
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Low Dimensional Discriminative Reranking
... our models are able to beat the rerank- ing approaches that use potentially more informa- tive alignment-based ...our models by posing the problem as a feature selection problem on the covariance matrices ...
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