基于改进麻雀搜索算法优化BP神经网络的土壤有机质空间分布预测

土壤有机质是衡量土壤肥力的重要指标,提高区域有机质空间分布预测精度十分必要.利用黄河流域卫宁平原采集的1 690个土壤表层（0~20 cm）有机质及自然环境、人类活动数据,通过1 348个点采用经典统计学、确定性插值、地统计插值和机器学习的方法分别建立了土壤有机质空间分布预测模型,以342个样点数据为测试集检验分析不同模型预测精度.结果表明,卫宁平原土壤表层ω（SOM）的平均值为14.34 g·kg^-1,1 690个采样点土壤有机质变异系数为34.81%,为中等程度变异,呈现出东北部、西南地区含量低,中间黄河左右岸和地势平缓的黄河阶地相对含量高的空间分布趋势.4类方法的预测精度大小为：机器学习法>地统计插值方法>确定性插值方法>经典统计学方法.通过对比,基于改进麻雀搜索算法优化的BP神经网络预测精度最好,改进后的麻雀搜索算法具有更优的收敛精度,避免了陷入局部最优,防止了数据过拟合,具有较好的预测能力,该优化算法可以提高土壤有机质含量预测精度,在土壤属性预测上有良好的应用前景.

Prediction Spatial Distribution of Soil Organic Matter Based on Improved BP Neural Network with Optimized Sparrow Search Algorithm

Soil organic matter is an important indicator of soil fertility, and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study, we analyzed the organic matter content of 1 690 soil surface layers (0-20 cm) and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics, deterministic interpolation, geostatistical interpolation, and machine learning, respectively, and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models. The results showed that the average SOM content of the surface soil of the Weining Plain was 14.34 g·kg^-1, and the average soil organic matter variation across 1 690 sampling points was 34.81%, indicating a medium degree of variability. The results also revealed a spatial distribution trend, with low soil organic matter content in the northeast and southwest, high soil organic matter on the left and right banks of the Yellow River in the middle, and relatively high soil organic matter in the sloping terrain of the Weining Plain. The four types of methods in order of high to low prediction accuracy were the machine learning method, geostatistical interpolation method, deterministic interpolation method, and classical statistical method. Through comparison, the BP neural network that was improved based on the optimized sparrow search algorithm had the best prediction accuracy, and the optimized sparrow search algorithm had better convergence accuracy, avoided falling into local optimization, prevented data overfitting, and had better prediction ability. This optimization algorithm can improve the accuracy of SOM prediction and has good application prospects in soil attribute prediction.