Heat Island Intensity Prediction in an Intelligent Sponge Urban System in the Qinghai-Tibet Plateau
Acronym
HISISP-QTP
Name
Heat Island Intensity Prediction in an Intelligent Sponge Urban System in the Qinghai-Tibet Plateau
Description
The Qinghai-Tibet Plateau, known for its high altitude, cold climate, and fragile ecosystem, presents unique challenges and opportunities for the implementation of an intelligent sponge urban system. The heat island effect, a phenomenon where urban areas experience higher temperatures compared to surrounding rural areas, can be particularly problematic in such a sensitive environment. Predicting and mitigating heat island intensity is crucial for improving urban livability and environmental sustainability.To develop a procedure for predicting heat island intensity in an intelligent sponge urban system, ensuring accurate and real-time predictions through a series of steps. Collect parameter information of the underlying surface using meteorological observation data from the sponge city, field observation data, and investigation data of the sponge city. Gather comprehensive data on the physical and environmental characteristics of the urban surface.Establish a set of digital labels with feature data derived from the collected information.Add the labeled data to the training sample set for the prediction model of sponge city surface heat island intensity. A crucial input for establishing a real-time prediction model.Train the prediction model function for sponge city surface heat island intensity using the data. Apply global pooling and average pooling to the input parameters of the heat island intensity prediction model to extract meaningful features. Add attention modules to the network to enhance the model's focus on relevant features. Adjust the network modules using an interpolation algorithm to ensure accurate feature representation. Subtract the normalized two matching features element by element and remove the features that do not meet the mutual matching criteria.Save the revised and updated function of the sponge city heat island prediction. This project provides a robust framework, combining advanced data collection, feature extraction, and machine learning.