基于机器学习的紫色土坡面水沙预测模型研究
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

S157

基金项目:

国家自然科学基金项目(U2040208、52009104)和陕西省水利科技计划项目(2022slkj-04)资助


Research on Water and Sand Prediction Model of Purple Soil Slope Based on Machine Learning
Author:
Affiliation:

Fund Project:

Supported by the National Natural Science Foundation of China (Nos.U2040208, 52009104); Shaanxi Provincial Water Conservancy Science and Technology Project (No.2022slkj-04)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为了精准预测土壤侵蚀变化、科学合理地预防水土流失的发生,为紫土坡地水土流失防治提供参考,基于典型紫土径流小区43场降雨资料,将雨量、历时和I30为特征指标,采用K-means聚类算法对降雨分类;使用随机森林(Randomforest,RF)算法评估各影响因子对径流深(H)、土壤流失量(S)的重要性,结合通径分析法进行重要因子筛选;将筛选所得关键因子作为模型输入变量,H、S为输出,使用BP神经网络构建预测模型。结果表明:(1)划分出3种雨型,B雨型(短历时、大雨强、小雨量)是主要降雨类型,A雨型(长历时、中雨强、大雨量)最剧烈。(2)各雨型下特征因子对H、S的重要程度明显不同。A雨型下T对S重要程度最高(31%),各因素对H的重要程度相对均匀;B雨型下各因素对S的影响差异小,F对H的重要程度最高(29%)。在C雨型下,Pr对H、S的重要程度最高(33%、36%)。(3)三种雨型下的H、S均受到Pr的显著影响,B、C雨型下的H、S分别同时受F和Vs、Ph的影响显著。(4)利用BP神经网络对H、S的预测精度均较高,Nash-Suttclife效率系数均高于0.95,且对H的预测模型精确度高于对S的预测模型。

    Abstract:

    Purple soil is one of the unique soils in China. Because of its low degree of soil development and poor soil erosion resistance, soil erosion in the purple soil area is very serious, which seriously hinders local agriculture and economic development. 【Objective】 To accurately predict soil erosion changes, we need to scientifically and rationally prevent the occurrence of soil erosion, provide a reference for the prevention and control of soil erosion on purple soil slopes, and promote research on soil and water conservation in purple soil areas. 【Method】 Based on the rainfall data of 43 typical purple soil run-off communities, rainfall, duration and I30 are used as characteristic indicators, and the K-means clustering algorithm was used to classify rainfall; the Random forest (RF) algorithm was used to evaluate the importance of each influencing factor on the depth of run-off (H) and soil loss (S), and the important factor screening was carried out in combination with the path analysis method. The key factors obtained from the screening were used as the input variables of the model, with H and S as the outputs. Also, a predictive model was constructed using BP neural network. 【Result】 The results showed that:(1) The erosive rainfall in the purple mound area in the southwest can be divided into three categories. Among them, the A rain type had a long duration, medium rain intensity, heavy rain volume, the lowest frequency of occurrence, and the greatest erosion, which was an important rain type that caused slope erosion in the area; the B rain type had a short duration, heavy rain, and light rain volume, and the most frequent occurrence, which was the main rain type that caused slope erosion in the area; and the C rain type, which had a medium duration, medium rain intensity, and medium rainfall, which occurred more frequently and also made a greater contribution to slope erosion in the area. (2) The importance of characteristic factors to H and S under each rain type was significantly different. For rain type A, the rain duration (T) was the most important to S (31%), and the importance of each factor to H was relatively uniform. Under rain type B, the influence of each factor on S was small, and the rainfall erosion force (F) was the most important to H (29%). Also, the degree of importance was the highest (29%). Under the rain type C, rainfall (Pr) was the most important to H and S(33%, 36%). (3) The H and S under the three rain types were significantly affected by Pr and the H and S under the B and C rain types were most significantly affected by F, vegetation cover (Vs), and average plant height (Ph) at the same time. (4) The prediction accuracy of H and S using the BP neural network was high, the efficiency coefficient of Nash-Suttclife is higher than 0.95, and the accuracy of the prediction model for H was higher than that of the prediction model for S. 【Conclusion】 Research on the purple soil mound area in the southwest needs to focus on preventing soil erosion caused by high-frequency heavy rainfall. In this study, the two models that used rainfall and other factors to calculate H and S had high forecasting capabilities, which provide technical support for soil erosion prediction to achieve accurate forecasting of soil erosion.

    参考文献
    相似文献
    引证文献
引用本文

陈琦涛,王添,李占斌,张皎,李鹏,李斌斌.基于机器学习的紫色土坡面水沙预测模型研究[J].土壤学报,2024,61(2):424-433. DOI:10.11766/trxb202207020361 CHEN Qitao, WAN Tian, LI Zhanbing, ZHANG Jiao, LI Peng, Li Binbin. Research on Water and Sand Prediction Model of Purple Soil Slope Based on Machine Learning[J]. Acta Pedologica Sinica,2024,61(2):424-433.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-06-22
  • 最后修改日期:2023-02-15
  • 录用日期:2023-06-15
  • 在线发布日期: 2023-07-17
  • 出版日期: 2024-03-15