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 (~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.
Highlights
O NE of the significant aspects of climate change is the globally rising temperature
By leveraging the historical air temperature data from in situ observations, the long short-term memory (LSTM) model can be exposed to more historical patterns that might not be present in the Internet of Things (IoT) observations
We selected the baseline models that are widely accepted by literature such as Hyndman and Athanasopoulos [34], Du et al [35], and Lyu et al [36], and they each represent a different type of time series forecasting model
Summary
O NE of the significant aspects of climate change is the globally rising temperature. Rising temperature and extreme heat events, exacerbated by the urban heat island effect, can produce life-threatening conditions to humans, overheat rivers, and increase risks to plants and wildlife. The urban heat island (UHI) is a phenomenon in which urban areas have higher temperatures (1–7 °F) than the surrounding rural areas [2]. Apart from the overall rising temperature, urban heat islands can be caused by reduced natural landscapes in urban areas, urban material properties that reflect less solar energy, urban geometries that hinder wind flow, and heat generated from human activities. Increasing risks of heat-related deaths and illnesses and growing demands of power exist in urban areas
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