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干旱区地理 ›› 2020, Vol. 43 ›› Issue (2): 398-405.doi: 10.12118/j.issn.1000-6060.2020.02.13

• 生物与土壤 • 上一篇    下一篇

陇中黄土丘陵沟壑区人工草地土壤水蚀预测模型

王钧1，2，李广1，聂志刚2，刘强2   

  1. 1 甘肃农业大学林学院，甘肃 兰州 730000； 2 甘肃农业大学信息科学技术学院，甘肃 兰州 730000
  • 收稿日期:2019-04-10 修回日期:2019-09-29 出版日期:2020-03-25 发布日期:2020-03-25
  • 通讯作者: 李广，男，博士，教授，从事水土保持与荒漠化防治研究.
  • 作者简介:王钧（1982-），男，甘肃白银人，博士研究生，讲师，从事水土保持与荒漠化防治研究.E-mail:julianwong82@163.com

Testing a soil water erosion predictive model in an artificial grassland-in the hillgully Loess Plateau

WANG Jun1，2,LI Guang1,NIE Zhi-gang2,LIU Qiang2   

  1. 1 College of Forestry,Gansu Agricultural University,Lanzhou 730000,Gansu,China; 2 College of Information Science and Technology,Gansu Agricultural University, Lanzhou 730000,Gansu,China

  • Received:2019-04-10 Revised:2019-09-29 Online:2020-03-25 Published:2020-03-25
  • Supported by:
    甘肃农业大学学科建设基金(GAUXKJS2018254,GAU-XKJS-2018258)；国家自然科学基金项目(31660348)；甘肃省重点研发计划(18YF1NA070)资助

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摘要：

针对陇中黄土丘陵沟壑区土壤水蚀过程复杂且难以有效预测的问题，以定西市安家沟水土保持试验站2005—2016年1~12月人工草地径流场试验数据为主要来源，将流域月降雨量、月侵蚀性降雨量、月径流量、月降雨强度、径流场面积、径流场坡度、土壤砂粒含量、土壤粘粒含量8个因子作为输入因子，月土壤水蚀量作为输出，运用偏最小二乘法(Partial Least-Squares Regression,PLSR)和长短期记忆(Long Short-Term Memory,LSTM)循环神经网络建立人工草地土壤水蚀预测模型，并利用BP(Back Propagation)、RNN(Recurrent Neural Network)、LSTM常见神经网络模型，对模型的有效性进行评估。结果表明：PLSR将模型8个输入因子减少为4个，从而有效解决LSTM神经网络模型对样本数量要求过高的问题； PLSR和LSTM神经网络模型的结合可以有效提高模型对人工草地土壤水蚀过程的预测精度和收敛速度，预测结果的平均相对误差小于4%，相关系数高于其他3种神经网络模型，而迭代次数、均方根误差和平均绝对误差均低于其他3种模型；研究发现坡度对人工草地土壤水蚀过程影响较为明显，降雨量小于25 mm时，人工草地土壤水蚀量不会随坡度增加而明显增长，但当降雨量超过25 mm时，人工草地土壤水蚀量会随坡度明显增加。 PLSRLSTM神经网络土壤水蚀预测模型可以准确预测陇中黄土丘陵沟壑区人工草地土壤水蚀量，为该地区水土流失的准确预报提供新的思路和方法。

关键词: 黄土丘陵沟壑区, 人工草地, 长短期记忆循环神经网络, 土壤水蚀预测

Abstract:

Erosion caused by soil water is widespread in the hillgully Loess Plateau of central Gansu,China,but it is difficult to observe and predict.The objectives of this study were to establish a predictive model for soil water erosion based on a partial least squares algorithm (PLSR) and long short-term memory (LSTM),and to evaluate the model’s effectiveness using a back-propagation neural network,a recurrent neural network,and a LSTM neural network.The results were verified with experimental data from a runoff field of artificial grassland in the soil and water conservation experimental stations in Anjiagou,Dingxi City,from 2005 to 2016 (1-12 months).Input factors for the model were monthly precipitation,monthly erosive precipitation,monthly runoff,monthly rainfall intensity,runoff area,slope of runoff field,soil sand content,and clay sand content ,from which the model predicted monthly soil water erosion.The results revealed that the original eight input factors could be reduced to four by the PLSR algorithm in the prediction model,alleviating the requirement of the LSTM neural network model for a large number of samples.Our method improved both the prediction precision and the convergence rate of the soil water erosion model in artificial grassland by combining a PLSR algorithm with a LSTM neural network.The average relative error of our prediction results was less than 4%,and the correlation coefficients were higher for our model than for the three neural network models used for comparison.In addition,iteration times,root mean square error,and mean absolute error were lower than for the other neural network models.We found that slope had a significant effect on soil water erosion,but only when the monthly rainfall was more than 25 mm.Our PLSR-LSTM neural network model could be used to accurately predict soil water erosion in artificial grassland in the hill-gully Loess Plateau of central Gansu,and it could provide a new path toward accurate prediction of soil erosion in this area.

Key words: hill-gully loess plateau; artificial grassland, long short-term memory (LSTM) recurrent neural network, soil water erosion prediction