基于遗传算法优化BP神经网络的石漠化区土壤水分动态预测模型

Dynamic prediction model of soil moisture in rocky desertification region based on BP neural network optimized by genetic algorithm

云南省建水县为典型的岩溶断陷盆地地貌,是我国喀斯特石漠化综合治理的重要类型区,对该地区土壤水分动态变化过程及其影响因素的揭示是水土保持和生态恢复工作的必要条件。收集云南建水县石漠化治理区2006年4月16日—2020年12月1日的逐日气象资料和轻度和中度2个石漠化区的土壤水分资料,分层建立BP(back propagation)神经网络土壤水分预测模型,并利用遗传算法对模型的权值和偏置进行优化。结果表明:1)经遗传算法优化后轻度石漠化区和中度石漠化区样地平均相对误差分别提升45%和63%,均方根误差分别提升3%和12%,R^(2)分别提升约27%和17%,但随着土层深度的增加,预测精度呈降低趋势;2)应用敏感性分析确定影响该地区土壤水分动态的主要气象因子为降水和气温,但随着土层深度增加,对气象因子的响应程度降低,遗传算法优化BP神经网络模型更适合对中度石漠化样地进行土壤水分预测。基于遗传算法优化的神经网络预测模型精度较高,能更好地实现该区域的预测。

[Background]Jianshui county of Yunnan province is a typical karst faulted basin landform,which is an important type area for the comprehensive control of karst rocky desertification in China.Because of the severe seasonal drought and rocky desertification,the barren water holding capacity of soil is poor and heterogeneity is high,and the prediction of soil water dynamic is difficult.It is of great significance for soil and water conservation and ecological restoration to reveal the process of soil water dynamic change and its influencing factors in this area.[Methods]Based on the daily meteorological data from April 16,2006 to December 1,2020 and soil moisture data from two different degrees of rocky desertification areas in Jianshui Karst rift basin of Yunnan province,a dynamic prediction model of soil moisture volume based on BP neural network was established for 0-10,10-20 and 20-30 cm soil layers.Genetic algorithm was used to optimize the weights and thresholds of the model.The default factor method was used for sensitivity analysis to identify the main meteorological factors affecting the prediction of soil water dynamics in this area.[Results]The BP neural network model optimized by genetic algorithm was used to predict the soil volume moisture content of mild rocky desertification area and moderate rocky desertification area from September 13,2019 to December1,2020.The results showed that the predicted value of the model was close to the measured value.Y_(MARE) increased by 45%and 63%,Y_(RMSE) by 3%and 12%,R^(2) by 27%and 17%,respectively.The simulation accuracy of soil water in 20-30 cm depth was improved most obviously,The sensitivity analysis showed that the sensitivity index of soil moisture to rainfall was the highest(1.317-1.735),followed by the sensitivity index to average temperature(0.8809-1.0712),followed by atmospheric pressure and solar radiation.[Conclusions]The simulation result of genetic algorithm optimization was improved obviously.The results show that the BP neural network model optimized by genetic algorithm can be well applied to soil moisture simulation in rocky desertification area,and the simulation accuracy is significantly improved compared with the non-optimized model.It is proved that the prediction accuracy of soil water in moderate rocky desertification plot is higher than that in mild rocky desertification plot,and the prediction accuracy decreases with the increase of soil depth.Sensitivity analysis was used to determine that precipitation was the main meteorological factor,followed by air temperature.The sensitivity analysis showed that the soil at 0-10 cm surface layer was the most sensitive to meteorological factors,while the soil at>10-20 cm middle layer was the most sensitive to meteorological factors.