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 (∼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.INDEX TERMS air temperature, Internet of Things (IoT), long short-term memory (LSTM), urban weather

Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review

“…Multiple linear regression can also be used in data analysis [53]. Currently, machine learning is commonly used to analyze sensorgenerated data [44,49,52,[55][56][57]63,[65][66][67]. The machine learning techniques include neural network [49], support vector machine [52], Long Short-Term Memory [56,67], and Random Forest [44].…”

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

“…The semantic result was IoT-based sensors and the proposed big data processing system makes it very efficient to monitor the manufacturing process [221]. Another example was proposed by Yu et al The processing is use LSTM (long short-term memory) deep learning network methods to process all IoT data and provide semantic services such as New York City temperature forecasting [222]. Besides those two computational intelligence methods, there are a lot of other methods to process and provide semantic services such as KNN [223], DTMC (Discrete-time Markov chain) [224], SNN [225], CNN [226], Random Tree [227], and more.…”

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

“…Furthermore, two NN flavors are particularly well suited for ESP application due to the spatiotemporal nature of Earth system model output quantities of interest. These are recurrent NNs (RNNs) that are built to handle temporal predictions and forecasts (Vandal et al 2017;Shen, Liu, and Wang 2021;Yu et al 2021) and convolutional NNs (CNNs) that efficiently target spatial relationships and patterns (Ise and Oba 2019;Chattopadhyay, Hassanzadeh, and Pasha 2020;Steininger et al 2020;Baño-Medina, Manzanas, and Gutiérrez 2021). Finally, generative modeling approaches, such as GANs (Besombes et al 2021;Klemmer et al 2021;Wang, Tang, and Gentine 2021) and VAEs (Tibau Alberdi et al 2018;Scher 2018;Mooers et al 2020;Zadrozny et al 2021) have demonstrated early promise in the Earth sciences (e.g., ).…”