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Your search keyword '"Gómez-Hernández, J. Jaime"' showing total 416 results
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416 results on '"Gómez-Hernández, J. Jaime"'

3. Ensemble random forest filter: An alternative to the ensemble Kalman filter for inverse modeling

Author

Godoy, Vanessa A., Napa-García, Gian F., and Gómez-Hernández, J. Jaime

Subjects
Computer Science - Machine Learning, J.2.2
Abstract

The ensemble random forest filter (ERFF) is presented as an alternative to the ensemble Kalman filter (EnKF) for the purpose of inverse modeling. The EnKF is a data assimilation approach that forecasts and updates parameter estimates sequentially in time as observations are being collected. The updating step is based on the experimental covariances computed from an ensemble of realizations and the updates are given as linear combinations of the differences between observations and forecasted system state values. The ERFF replaces the linear combination in the update step with a non-linear function represented by a random forest. In this way, the non-linear relationships between the parameters to be updated and the observations can be captured and a better update produced. The ERFF is demonstrated for the purpose of log-conductivity identification from piezometric head observations in a number of scenarios with varying degrees of heterogeneity (log-conductivity variances going from 1 up to 6.25 (ln m/d)2), number of realizations in the ensemble (50 or 100), and number of piezometric head observations (18 or 36). In all scenarios, the ERFF works well, being able to reconstruct the log-conductivity spatial heterogeneity while matching the observed piezometric heads at selected control points. For benchmarking purposes the ERFF is compared to the restart EnKF to find that the ERFF is superior to the EnKF for the number of ensemble realizations used (small in typical EnKF applications). Only when the number of realizations grows to 500, the restart EnKF is able to match the performance of the ERFF, albeit at triple the computational cost., Comment: Submitted to Journal of Hydrology 26 pages, 14 figures

Published
2022

4. Spatial Modeling of Travel Demand Accounting for Multicollinearity and Different Sampling Strategies: A Stop-Level Case Study

Author

de França Marques, Samuel, Pitombo, Cira Souza, and Gómez-Hernández, J. Jaime

Subjects
Bus lines -- Case studies, Land use -- Planning, Public transportation -- Case studies, Transportation industry
Abstract

Stop-level ridership data serve as a basis for various studies toward increasing bus patronage and promoting sustainable land use planning. To address limitations found in previous studies, this study proposes a novel approach based on Geographically Weighted Principal Component Analysis (GWPCA) and Ordinary Kriging to predict the stop-level boarding or alighting data along bus lines in São Paulo (Brazil), considering four different sampling methods. The main contributions are as follows: by accounting for the spatial heterogeneity of the predictor dataset, the GWPCA can identify the most important factor affecting transit ridership even in bus stops with no information on boarding and alighting; the spatial modeling of stop-level ridership data using GWPCA components as explanatory variables allows visualizing the spatially varying effects from predictors on ridership, supporting the land use planning at a local level; GWPCA coupled with kriging simultaneously addresses the multicollinearity of predictor data, its spatial heterogeneity, and the spatial dependence of the stop-level ridership variable, thus enhancing the goodness-of-fit measures of the transit ridership prediction in unsampled stops; and a balanced sample on predictor data and well-spread in the geographic space might be preferred to accurately estimate missing stop-level ridership data. In addition to solve the lack of stop-level ridership data, supporting a reliable bus system planning, the proposed method indicates what predictors should be addressed by policymakers to stimulate a transit-oriented development. The method can be successfully applied to other travel demand variables facing a lack of data such as traffic volume in road segments and mode choice at the household level., Author(s): Samuel de França Marques (corresponding author) [1]; Cira Souza Pitombo [1]; J. Jaime Gómez-Hernández [2] 1. Introduction Stop-level boarding and alighting data are important pieces of information for decisions [...]

Published
2024
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10. Ensemble Kalman Filtering

Author

Gómez-Hernández, J. Jaime, Finkl, Charles W., Series Editor, Fairbridge, Rhodes W., Series Editor, Daya Sagar, B. S., editor, Cheng, Qiuming, editor, McKinley, Jennifer, editor, and Agterberg, Frits, editor

Published
2023
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11. Sequential Gaussian Simulation

Author

Gómez-Hernández, J. Jaime, Finkl, Charles W., Series Editor, Fairbridge, Rhodes W., Series Editor, Daya Sagar, B. S., editor, Cheng, Qiuming, editor, McKinley, Jennifer, editor, and Agterberg, Frits, editor

Published
2023
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12. Random Function

Author

Gómez-Hernández, J. Jaime, Finkl, Charles W., Series Editor, Fairbridge, Rhodes W., Series Editor, Daya Sagar, B. S., editor, Cheng, Qiuming, editor, McKinley, Jennifer, editor, and Agterberg, Frits, editor

Published
2023
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