Environmental data prediction algorithm based on adaptive linear model
To address the issues of real-time performance and accuracy in the application of environmental big data in smart cities,an environmental data prediction algorithm based on an adaptive linear model was proposed.The model was trained according to the real-time changes in meteorological data,with the training window size being adaptively adjusted.A dynamic and real-time switch between training and prediction states was implemented,enhancing the model's adaptability to environmental changes.The algorithm featured lower latency and reduced computational overhead,allowing for direct deployment on sensor nodes to meet the real-time requirements of data prediction.Simulation experiments constructed on real environmental datasets showed that,compared to fixed-window models,the proposed algorithm reduced data prediction error by more than 17.4%,decreased the energy consumption of environmental data collection by over 80%,and reduced the average latency by more than 50%.When compared to existing machine learning algorithms,the training and prediction time of the proposed algorithm was reduced by more than 37%.
smart cityenvironmental big dataedge servicelinear predictionenergy saving
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