Naratip Santitissadeekorn
Biographic Data
| ID | 9576496 |
|---|---|
| NAME | Naratip Santitissadeekorn |
| GIVEN NAMES | Naratip |
| FAMILY NAME | Santitissadeekorn |
| SIGNATURE | SANTITISSADEEKORN N |
| AFFILIATIONS | University of Surrey |
| ORCID | 0000-0001-5579-0458 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
A data-driven framework to manage uncertainty due to limited transferability in urban growth models
The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scena…
Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation
Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibra…
No prominent works on this page.
Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation
Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibra…
A data-driven framework to manage uncertainty due to limited transferability in urban growth models
The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scena…
Bayesian probability (2 works) · Calibration (2 works) · Computer Science (2 works) · Land Use and Ecosystem Services (2 works) · Mathematics (2 works) · Spatial and Panel Data Analysis (2 works) · Statistics (2 works) · Algorithm (1 work) · Approximate Bayesian Computation (1 work) · Artificial Intelligence (1 work)