Prediction of Soil Heavy Metal Content Based on Deep Reinforcement Learning

Abstract

Since the prediction accuracy of heavy metal content in soil by common spatial prediction algorithms is not ideal, a prediction model based on the improved deep Q network is proposed. The state value reuse is used to accelerate the learning speed of training samples for agents in deep Q network, and the convergence speed of model is improved. At the same time, adaptive fuzzy membership factor is introduced to change the sensitivity of agent to environmental feedback value in different training periods and improve the stability of the model after convergence. Finally, an adaptive inverse distance interpolation method is adopted to predict observed values of interpolation points, which improves the prediction accuracy of the model. The simulation results show that, compared with random forest regression model (RFR) and inverse distance weighted prediction model (IDW), the prediction accuracy of soil heavy metal content of proposed model is higher by 13.03% and 7.47%, respectively.