Data prediction of soil heavy metal content by deep composite model
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SOILS, SEC 5 • SOIL AND LANDSCAPE ECOLOGY • RESEARCH ARTICLE
Data prediction of soil heavy metal content by deep composite model Wenqi Cao 1 & Cong Zhang 1 Received: 16 April 2020 / Accepted: 28 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose The content of heavy metals in the soil is directly related to the control of soil pollution, but due to the limitations of manpower and material resources, it is difficult to detect them in detail; researchers usually need to predict the content of soil heavy metals in unknown areas based on existing data. Therefore, how to choose an effective method to complete this process has become a challenging problem. Materials and methods In this paper, a deep composite model (DCM) is proposed. The model is based on radial basis function neural network (RBFNN), then, uses self-adaptive learning based particle swarm optimization algorithm (SLPSO) to generate the weight and bias of the output layer of RBFNN and employs adaptive adjustment based root mean square back-propagation (ARMSProp) to optimize all variables of RBFNN, so as to improve the prediction accuracy of the model for soil heavy metal content. When using this model to predict soil heavy metal content, the Pearson coefficient is used as a comparison index to compare the correlation between different heavy metals and heavy metals to be predicted, and finally the content of heavy metals with a Pearson coefficient greater than 0.5 is selected as the input of the model variable. Results and discussion First in the validation of the proposed SLPSO algorithm, the effectiveness of SLPSO and the feasibility of being applied to the DCM model have been proved. Then, the DCM was applied to the prediction of soil heavy metal content in six new urban areas of Wuhan in China, the experimental results show that the predicted value of soil heavy metal content of DCM is closer to the actual value than other comparison models, and the four error indicator values of DCM are also significantly lower than other comparison models, especially when compared with RBFNN, the MAPE and SMAPE of DCM have dropped by 8.6% and 3.9%, respectively. Conclusions We can conclude that the deep composite model proposed in this paper obtains a good prediction accuracy when predicting soil heavy metal content; it has certain feasibility and can be used as an effective method for soil heavy metal content prediction. Keywords Deep composite model . Prediction of soil heavy metal content . Radial basis function neural network . Particle swarm optimization . Root mean square back-propagation
1 Introduction In the past few decades, with the development of industry, soil heavy metal pollution caused by industrial pollution has become more and more serious (Chodak et al. 2007). The heavy
Responsible editor: Jun Zhou * Cong Zhang [email protected] 1
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
metals that cause pollution to the soil environment are mainly As, Cr, Cu, Ni, Pb,
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