IDP: An Intelligent Data Prediction Scheme Based on Big Data and Smart Service for Soil Heavy Metal Content Prediction

In the application of regression prediction through big data technology, the error between the predicted value and the true value is often large. In order to reduce the error of data prediction, this paper proposes an Intelligent Data Prediction (IDP) scheme for Smart Service. It uses Least Squares Support Vector Machine (LSSVM) as the basic prediction model. Since there is no standard procedure for determining the main parameters of LSSVM, an improved Particle Swarm Optimization (MBPSO) algorithm is used to simultaneously optimize the parameters of LSSVM. The main disadvantage of PSO is precocity due to the disappearance of population diversity. Based on this, Improvement strategy of MBPSO aims to continuously generate “More” and “Better” particles. First, in order to avoid the early disappearance of particle diversity, MBPSO re-adjusted the inertia weight and learning factor. Secondly, a renewable access strategy is proposed to allow a part of the disappeared population to regenerate. Finally, the method of global optimal adjustment is introduced to help particles find the optimal flight direction. In order to verify the effectiveness of MBPSO, 9 test functions are used to test the algorithm performance. The results show that MBPSO's optimization speed, best and mean all perform best. Taking the farmland soil heavy metal data sets of Dongxihu District and Hannan District of Wuhan City as examples of application, the content of heavy metals Cr and Pb in the soil was predicted. The results show that the predicted value of IDP is closer to the actual value, and the three error index values are significantly lower than other models. Especially in the prediction of Pb content, compared with the LSSVM model, the prediction errors of the two regions are reduced by 25.67% and 20.70% respectively. We can conclude that the proposed IDP scheme has practical significance in data prediction.