基于NGO-CNN-SVM的高标准农田灌溉工程施工成本预测  

Predicting the construction cost of high standard farmland irrigation projects using NGO-CNN-SVM

作　　者：韩坤 王惟璐 黄雪峰 李鹏海 李春生[1] 郑俊林[1] HAN Kun;WANG Weilu;HUANG Xuefeng;LI Penghai;LI Chunsheng;ZHENG Junlin(College of Water Conservancy,Shenyang Agricultural University,Shenyang 110866,China)

机构地区：[1]沈阳农业大学水利学院,沈阳110866

出　　处：《农业工程学报》2024年第14期62-72,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(52209063);辽宁省地方标准立项计划项目(2021186)。

摘　　要：为提高高标准农田项目施工成本的预测精度,控制施工成本在合理范围,减少投资风险,该研究从单体灌溉工程施工成本预测角度出发,通过随机森林(random forest,RF)筛选出高标准农田灌溉工程施工成本的关键影响因素,结合卷积神经网络(convolutional neural networks,CNN)和支持向量机(support vector machine,SVM)两种模型的优点,通过北方苍鹰优化算法(northern goshawk optimization,NGO)对模型里的惩罚因子和核参数进行寻优,构建基于NGO-CNN-SVM的施工成本预测模型。通过辽宁省2018—2023年高标准农田工程中灌溉工程的施工成本数据,选取样本决定系数R^(2)、平均绝对误差MAE、平均绝对百分比误差MAPE和均方根误差RMSE作为精度指标进行分析,结果表明:基于NGO-CNN-SVM的施工成本预测模型在渠道工程中MAE低于0.615万元,RMSE低于0.512万元,R^(2)达到0.968以上,相对误差小于4.210%;在进水闸工程中MAE低于0.610万元,RMSE低于0.536万元,R^(2)达到0.966以上,相对误差小于4.410%;在桥涵工程中MAE低于0.494万元,RMSE低于0.477万元,R^(2)达到0.970以上,相对误差小于3.548%,并相比较于反向传播神经网络,CNN和CNN-SVM模型,NGO-CNN-SVM模型的预测结果均最优。通过特征选择、模型融合、算法优化以及不同模型对比表明NGO-CNN-SVM模型具有更高的预测准确率和泛化性,可为高标准农田灌溉工程施工成本预测提供理论依据。Construction cost is difficult to predict in high-standard farmland projects,due to the short construction period.In this study,the construction cost was predicted for the single-unit irrigation projects,in order to improve the prediction accuracy.The key influencing factors were then screened from the construction cost of high-standard farmland irrigation projects using random forest(RF).Then,convolutional neural networks(CNN)and support vector machine(SVM)were combined to construct a CNN-SVM-based prediction model,in order to improve the prediction accuracy over a single model.The penalty parameter C and kernel function parameter g of the CNN-SVM model were optimized by northern goshawk optimization(NGO).The NGO shared the higher convergence speed and stronger optimization,compared with the rest.Finally,the prediction model(NGO-CNN-SVM)was obtained for the construction cost of high-standard farmland irrigation projects.The data was collected from the irrigation project in the high-standard farmland in Liaoning Province from 2018 to 2023.Coefficient of determination(R^(2)),mean absolute error(MAE),mean absolute percentage error(MAPE),and root mean square error(RMSE)were taken as the accuracy indexes for analysis.The results show that the NGO-CNN-SVM model of construction cost in the channel project shared the MAE and RMSE lower than 0.615 and 0.512 million yuan,respectively,where the R^(2) reached more than 0.968,and the relative error was less than 4.21%;In the project of the inlet sluice gate,the MAE and RMSE were lower than 0.610 and 0.536 million yuan,respectively,where the R^(2) reached more than 0.966,and the relative error was less than 4.41%;In the project of bridge and culvert,the MAE and RMSE were less than 0.494 and 0.477 million yuan,where the R^(2) reached more than 0.970,and the relative error was less than 3.548%.Taking channel engineering as an example,the deep learning network model(CNN)was reduced by about 34%,20%,33% in the MAE,RMSE,MAPE,and improved by 3% for the R^(2),respectively,compared w

关 键 词：高标准农田 灌溉 随机森林 北方苍鹰优化算法 卷积神经网络 支持向量机 施工成本 

分 类 号：F303[经济管理—产业经济] S277[农业科学—农业水土工程] TV93[农业科学—农业工程]