Identification

Title

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

Abstract

The rising temperature is one of the key indicators of a warming climate, capable of causing extensive stress to biological systems as well as built structures.Ambient temperature collected at ground level can have higher variability than regional weather forecasts, which fail to capture local dynamics. There remains a clear need for accurate air temperature prediction at the suburban scale at high temporal and spatial resolutions. This research proposed a framework based on a long short-term memory (LSTM) deep learning network to generate day-ahead hourly temperature forecasts with high spatial resolution. Air temperature observations are collected at a very fine scale (similar to 150m) along major roads of New York City (NYC) through the Internet of Things (IoT) data for 2019-2020. The network is a stacked two layer LSTM network, which is able to process the measurements from all sensor locations at the same time and is able to produce predictions for multiple future time steps simultaneously. Experiments showed that the LSTM network outperformed other traditional time series forecasting techniques, such as the persistence model, historical average, AutoRegressive Integrated Moving Average (ARIMA), and feedforward neural networks (FNN). In addition, historical weather observations are collected from in situ weather sensors (i.e., Weather Underground, WU) within the region for the past five years. Experiments were conducted to compare the performance of the LSTM network with different training datasets: 1) IoT data alone, or 2) IoT data with the historical five years of WU data. By leveraging the historical air temperature from WU, the LSTM model achieved a generally increased accuracy by being 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.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.org/ark:/85065/d7rr22qj

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2021-09-29T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact