Research On Forecasting And Reconstruction Of Environmental Field Using Deep Attention Neural Networks

Environmental monitoring,as a cutting-edge technique for evaluating environmental changes and enabling timely degradation warnings,holds paramount research value and promising application prospects.However,traditional methods that rely on simple rules to estimate environmental changes no longer satisfy the demands of increasingly complex realworld scenarios.With the remarkable advances of deep learning in both theoretical and practical study,it provides effective support for processing and understanding complex environmental data and corresponding modeling.In the domain of environmental monitoring,deep learning offers robust modeling capabilities and precise prediction potential for intricate real-world multivariate environmental field data.As a result,it carries significant theoretical implications and practical significance for environmental field prediction and reconstruction.This paper focuses on addressing challenges in environmental image time series prediction and fast active sampling planning method.It conducts a comprehensive analysis of the underlying factors and proposes targeted deep learning methodologies.The main research contributions and innovations can be summarized in the following three aspects:(1)This paper proposes a deep neural network based on spatiotemporal attention(STAN)for predicting sequential environmental scene maps.By taking a sequence of historical environmental maps as input,STAN utilizes attention mechanisms to extract rich latent spatiotemporal feature representation.With the design of a multi-step iterative network structure,STAN achieves multi-step prediction of future environmental maps.(2)In order to maximize the utilization of ground truth labels and constrain the convergence direction of the network,this paper introduces a teacher forcing learning strategy.During the training phase,this strategy incorporates real labels as auxiliary inputs,effectively decoupling the training and testing phases while maintaining their effectiveness.By adopting this approach,the multi-step prediction network benefits from a flexible input mode,leading to accelerated convergence speed and enhanced prediction performance.(3)To address the environmental field reconstruction problem,this paper proposes a fast proactive sampling planning method based on deep attention neural networks.This method leverages Gaussian Markov processes to describe the environmental field,utilizing the covariance function of the random field to capture spatial interaction information and determine the optimal observation locations with maximum mutual information.To capture the interplay between the precision matrix and the observation locations with maximum information,this paper takes a comprehensive approach by considering both global and local spatial levels in the input matrix.By designing an attention-based deep neural network,it achieves online planning for the optimal perception region.The above methods are trained and validated on public datasets,and experimental results show the effectiveness,proactivity,and efficiency of the proposed methods in time-series environmental field prediction and reconstruction tasks.At the same time,this paper also implements a prototype system in a real indoor temperature monitoring scenario to further explore and verify the practical performance of the proposed methods.