Prediction of Spatial Distribution of Soil Heavy Metal Contents in Mining Areas Based on Genetic Algorithm and BP Neural Network
Based on genetic algorithm(GA)and back propagation neural network(BPNN),this study proposed a composite model:GABP model.Focusing on a mining area and its surroundings in Chizhou City,Anhui Province,the spatial distribution of soil pH value and the concentrations of seven heavy metals(Cd,Pb,Cr,Cu,Ni,Hg and As)were predicted by GABP model,and the prediction results were compared with those of BPNN and inverse distance weighting(IDW)method.The results showed that,influenced by mining activities,there was significant spatial heterogeneity in soil pH value and heavy metal concentrations in the study area.The data augmentation of GABP model effectively compensated for the dependency of BPNN on the sample size,and simultaneously incorporated geographical location and elevation attributes.The precision evaluation results indicated that the average R2,r,RMSE and MAE of GABP model was 3.03 times and 2.56 times,2.93 times and 2.39 times,0.85 times and 0.61 times,0.79 times and 0.62 times higher than those of IDW and BPNN,respectively,indicating a higher predictive accuracy.The proposed model can solve the issues in traditional spatial interpolation methods where negative values and boundary interpolation difficulties may occur,and provides a novel approach for predicting the spatial distribution of soil heavy metal contents.
Genetic algorithmBP neural networkGABP modelSpatial distribution predictionHeavy metal content
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