农业工程学报2024，Vol.40Issue(14)：62-72,11.DOI:10.11975/j.issn.1002-6819.202312025
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
摘要
Abstract
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(R2),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 R2 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 R2 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 R2 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 R2,respectively,compared with the traditional back propagation neural networks.A higher efficiency of prediction was achieved in the less complexity and nonlinear data dimension.In addition,the CNN-SVM model was significantly reduced by about 40％,46％,41％in the MAE,RMSE,MAPE,and improved by 4％for the R2,respectively,compared with the model CNN,indicating better performance than that of the single model.Compared with the CNN-SVM model,the MAE,RMSE,MAPE of the NGO-CNN-SVM model were reduced by about 22％,25％,17％,and improved by 4％for the R2,respectively,indicating that the randomly generated hyperparameters led to the low generalization and prediction accuracy.The optimal optimization of the hyperparameters had further improved the performance of the improved model.In summary,the feature selection,model fusion,optimization,and comparison of different models show that the NGO-CNN-SVM model shared higher prediction accuracy and generalization.The model can be applied to directly establish the index system,in order to estimate high-standard farmland projects.The finding can provide the theoretical basis and support to formulate the construction cost and budget quota of irrigation projects in high-standard farmland.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Key words
high-standard farmland/irrigation/random forest/northern goshawk optimization/convolutional neural networks/support vector machine/construction cost分类
管理科学引用本文复制引用
韩坤,王惟璐,黄雪峰,李鹏海,李春生,郑俊林..基于NGO-CNN-SVM的高标准农田灌溉工程施工成本预测[J].农业工程学报,2024,40(14):62-72,11.基金项目
国家自然科学基金项目(52209063) （52209063）
辽宁省地方标准立项计划项目(2021186) （2021186）
