西南地区紫色土阳离子交换量预测模型的研究

Prediction Models for Cation Exchange Capacity of Purplish Soils in Southwest China

  • 摘要:
    目的 为了提高紫色土阳离子交换量(CEC)的预测精度,建立高效的紫色土CEC预测模型。
    方法 利用中国西南地区171个紫色土样的土壤pH、有机质含量和土壤颗粒组成数据,采用遗传算法(GA)与Piecewise映射和Levy飞行优化粒子群算法(PLPSO)的混合策略优化反向传播神经网络(BPNN)和经典统计学方法分别构建MLR、BPNN、GA-BPNN、PLPSO-BPNN、PSO-GA-BPNN、PLPSO-GA-BPNN紫色土CEC预测模型,以平均绝对百分比误差(MAPE)、均方根误差(RMSE)和决定系数(R²)作为模型的评价指标。
    结果 六种紫色土CEC预测方法,MAPE排序为PLPSO_GA-BPNN(0.6167) < PSO_GA-BPNN(0.7421) < PLPSO-BPNN(0.9871) < GA-BPNN(1.0821) < BPNN(1.2561) < MLR(1.7772);RMSE排序为PLPSO_GA-BPNN(0.1112)< PSO_GA-BPNN(0.1254)< GA-BPNN(0.1349)< PLPSO-BPNN(0.1441)< BPNN(0.1894)< MLR(0.2557);R²排序为MLR(0.5982)< BPNN(0.7314)< GA-BPNN(0.8638)< PLPSO-BPNN(0.8745)< PSO_GA-BPNN(0.8923)< PLPSO_GA-BPNN(0.9174)。PLPSO_GA-BPNN在MAPE、RMSE和R²上均表现最优,分别为0.6167、0.1112和0.9174,显著优于其他模型。
    结论 综上所述,PLPSO_GA-BPNN模型预测性能最好,PLPSO_GA的优化效果最佳。该模型不仅为精准预测紫色土CEC提供更直接有效的方法,而其在土壤属性预测领域具有广阔的应用前景。

     

    Abstract:
    Objective The aim was to establish an efficient predicting model for cation exchange capacity (CEC) in purplish soils.
    Method The data from 171 purplish soil samples in the southwest region of China, including soil pH, organic matter content, and soil particle composition, have been employed a hybrid strategy of genetic algorithm (GA) and Piecewise mapping with Levy flight optimized Particle Swarm Optimization (PLPSO) to optimize the Back Propagation Neural Network (BPNN). Additionally, the classical statistical method was used to construct multiple models for predicting CEC in purplish soils, including MLR, BPNN, GA-BPNN, PLPSO-BPNN, PSO-GA-BPNN and PLPSO-GA-BPNN. The models were evaluated using the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of Determination (R2).
    Result The ranking of MAPE for the CEC prediction methods was as follows: PLPSO_GA-BPNN (0.6167) < PSO_GA-BPNN (0.7421) < PLPSO-BPNN (0.9871) < GA-BPNN (1.0821) < BPNN (1.2561) < MLR (1.7772). The RMSE ranking was: PLPSO_GA-BPNN (0.1112) < PSO_GA-BPNN (0.1254) < GA-BPNN (0.1349) < PLPSO-BPNN (0.1441) < BPNN (0.1894) < MLR (0.2557). The R2 ranking was: MLR (0.6182) < BPNN (0.7313) < GA-BPNN (0.8637) < PLPSO-BPNN (0.8745) < PSO_GA-BPNN (0.8923) < PLPSO_GA-BPNN (0.9074). The PLPSO_GA-BPNN model performed the best in terms of RMSE and R2, with values of 0.6167, 0.1112, and 0.9074, respectively.
    Conclusion The PLPSO_GA-BPNN model demonstrated the best predictive performance, and the optimization effect of PLPSO_GA was the most effective, which not only providing a more direct and effective method for accurately predicting CEC in purplish soils, but also holding broad application prospects in the field of soil property prediction.

     

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