Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for
localized surface temperature forecasting in an urban environment
- URL: http://arxiv.org/abs/2102.02892v1
- Date: Thu, 4 Feb 2021 21:21:21 GMT
- Title: Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for
localized surface temperature forecasting in an urban environment
- Authors: Manzhu Yu, Fangcao Xu, Weiming Hu, Jian Sun, Guido Cervone
- Abstract summary: 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.
- Score: 29.94873599943544
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rising temperature is one of the key indicators of a warming climate, and
it can cause extensive stress to biological systems as well as built
structures. Due to the heat island effect, it is most severe in urban
environments compared to other landscapes due to the decrease in vegetation
associated with a dense human-built environment. It is essential to adequately
monitor the local temperature dynamics to mitigate risks associated with
increasing temperatures, which can include short term strategy to protect
people and animals, to long term strategy to how to build a new structure and
cope with extreme events. Observed temperature is also a very important input
for atmospheric models, and accurate data can lead to better future forecasts.
Ambient temperature collected at ground level can have a higher variability
when compared to regional weather forecasts, which fail to capture the local
dynamics. There remains a clear need for an accurate air temperature prediction
at the sub-urban scale at high temporal and spatial resolution. 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. By leveraging
the historical air temperature data from in-situ observations, the LSTM model
can be exposed to more historical patterns that might not be present in the IoT
observations. Meanwhile, by using IoT observations, the spatial resolution of
air temperature predictions is significantly improved.
Related papers
- Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network [0.11249583407496218]
This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensing inputs.
Experimental results validate the model's proficiency in predicting temperature patterns, achieving a Mean Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution of 20 meters per pixel.
arXiv Detail & Related papers (2024-05-31T09:39:41Z) - Advancing Data-driven Weather Forecasting: Time-Sliding Data
Augmentation of ERA5 [3.3748750222488657]
We introduce a novel strategy that deviates from the common dependence on high-resolution data.
This paper improves on conventional approaches by adding more variables and a novel approach to data augmentation and processing.
Our findings reveal that despite the lower resolution, the proposed approach demonstrates considerable accuracy in predicting atmospheric conditions.
arXiv Detail & Related papers (2024-02-13T03:01:22Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method [66.80344502790231]
We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset.
Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors.
Our proposed method outperforms other specially designed baseline models on multiple surface variables.
arXiv Detail & Related papers (2024-01-22T14:02:56Z) - Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach.
Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Learning to forecast vegetation greenness at fine resolution over Africa
with ConvLSTMs [2.7708222692419735]
We use a Convolutional LSTM (ConvLSTM) architecture to address this task.
We predict changes in the vegetation state in Africa using Sentinel-2 satellite NDVI, having ERA5 weather reanalysis, SMAP satellite measurements, and topography.
Ours results highlight how ConvLSTM models can not only forecast the seasonal evolution of NDVI at high resolution, but also the differential impacts of weather anomalies over the baselines.
arXiv Detail & Related papers (2022-10-24T23:03:36Z) - Forecasting large-scale circulation regimes using deformable
convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs)
We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future.
Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
arXiv Detail & Related papers (2022-02-10T11:37:00Z) - Deep Learning based Extreme Heatwave Forecast [8.975667614727648]
Using 1000 years of state-of-the-art PlaSim Planete Simulator Climate Model data, it is shown that Convolutional Neural Network-based Deep Learning frameworks, with large-class undersampling and transfer learning achieve significant performance in forecasting the occurrence of extreme heatwaves.
arXiv Detail & Related papers (2021-03-17T16:10:06Z) - DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
Earth system models (ESMs) are often used to generate future projections of climate change scenarios.
As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM.
Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
arXiv Detail & Related papers (2020-11-23T20:13:37Z) - Augmented Convolutional LSTMs for Generation of High-Resolution Climate
Change Projections [1.7503398807380832]
We present auxiliary informed-temporal neural architecture for statistical downscaling.
Current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India.
arXiv Detail & Related papers (2020-09-23T17:52:09Z)
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.