Improved LSTM Algorithm for WBGT Index Prediction in Smart Cities

With the development and application of Internet of Things (IoT) technology, IoT has been widely used in agriculture, industry, and urban construction. In the process of building Smart cities, setting up small-scale weather stations based on IoT technology can effectively monitor certain special environments where large weather stations cannot accurately assess and predict short-term extreme weather events. As the summer heat approaches, the number of heatstroke cases is continuously rising. The Wet Bulb Globe Temperature (WBGT) index, which is closely related to heatstroke, provides a simple method for evaluating the thermal work environment and thermal load of workers in hot conditions. In this paper, through adjusting the model structure and moving window, and optimizing the predictive model parameters, an improved Long Short Term Memory (LSTM) algorithm is proposed to forecast WBGT values at future time points: five minutes, thirty minutes, and sixty minutes ahead. The paper also conducts a simple analysis of the daily average performance of WBGT in relation to air humidity. Additionally, it compares dual-input models that include air humidity as input. Through a comparison of four prediction performance evaluation metrics, simple-input LSTM models demonstrate lower error.