Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring

• URL: http://arxiv.org/abs/2411.09726v2
• Date: Mon, 18 Nov 2024 07:50:45 GMT
• Title: Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring
• Authors: Federico P. Cortese, Antonio Pievatolo,
• Abstract summary: We introduce a-temporal handles that handle data with persistence across both spatial and temporal dimensions. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. Our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring.
• Score: 0.0
• License:
• Abstract: Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that clusters data with persistence across both spatial and temporal dimensions. This framework enhances interpretability, minimizes abrupt state changes, and easily handles missing data. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. When applied to hourly environmental data gathered from a set of weather stations located across the city of Singapore, our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring. The comparison of these regimes with feedback on thermal preference indicates the potential of an unsupervised approach to avoid extensive surveys.

Related papers

• A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation [23.504915709396204]
  We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics. We demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output.
  arXiv  Detail & Related papers  (2024-12-19T19:47:35Z)
• Urban Air Temperature Prediction using Conditional Diffusion Models [26.577558935382477]
  Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. Air temperature at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects $T_a$ is a critical research question which requires high-resolution (HR) $T_a$ data at neighborhood scale. We propose a novel method to predict HR $T_a$ at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from
  arXiv  Detail & Related papers  (2024-12-18T04:56:29Z)
• A Machine Learning Approach for the Efficient Estimation of Ground-Level Air Temperature in Urban Areas [6.7236795813629]
  The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models.
  arXiv  Detail & Related papers  (2024-11-05T15:05:23Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec [0.0]
  This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting indoor environmental preferences. The framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA) The results of a test implementation show 14-28% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.
  arXiv  Detail & Related papers  (2021-10-30T07:43:11Z)
• Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for localized surface temperature forecasting in an urban environment [29.94873599943544]
  This research proposed a framework based on Long Short-Term Memory (LSTM) deep learning network to generate day-ahead hourly temperature forecast with high spatial resolution. A case study is shown which uses historical in-situ observations and Internet of Things (IoT) observations for New York City, USA.
  arXiv  Detail & Related papers  (2021-02-04T21:21:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.