摘要
【目的】探究分类模型对灌溉渠道运行状况健康度的检测效果。【方法】对陕西关中地区某灌区2014年10月―2018年10月各级渠道流量以及灌溉发生运行不良状况下异常终端报警信息等数据进行抽取和探索分析处理,提取渠道运行状况特征评价指标,构建灌区渠道运行状况健康识别模型。【结果】构建的LM神经网络模型与传统BP网络模型、CART决策树模型,对759个训练样本分类中综合分类准确率均高于98%;对156个测试样本,综合分类准确率LM神经网络模型相比BP网络模型同为96.2%,CART决策树模型为94.9%。对3个模型测试样本ROC曲线分析发现,LM神经网络模型中运行正常渠道分类准确度折线与运行不正常渠道分类准确度折线表现更优。【结论】LM神经网络模型是最优模型,可实际应用于渠道运行状态健康度识别检测,实际应用中对该灌区运行不正常渠道识别准确率为80.95%。
【Background】An irrigation channel system normally comprises a main channel,channels,branch channels,buckets and other water-retaining buildings.The health of the channel system is related to water utilization efficiency of its irrigation areas.Channel leakage and other health issues in the system were traditionally diagnosed by manual inspection,which is not only tedious and laborious but also unable to determine the health status underneath the structures.It could hence cause erroneous diagnoses or miss heath issues due to the difference in experience between inspectors.Given than most modern irrigation districts have been automatically operated and that data such as channel flow,flow velocity and fluctuation of water level are automaticity recorded,it is feasible to diagnose the channel system using these archived data.【Objective】The overarching objective of this paper is to present and test a data mining model to diagnose heath status of irrigation channel system.【Method】Archived data measured from October 2014 to October 2018 in an irrigation district at Guanzhong,Shanxi province were used to demonstrate the model development and validation.We first extracted and analyzed the data for different channels and the alarms reported during the operation of all channels in the irrigation district,from which we constructed the health diagnose system based on different neural network models and the CART decision-tree model.【Result】The training of the LM neural network model,the traditional BP network model and the CART decision-tree model based on 759 training samples showed that the accuracy of all three models was higher than 98%.For normal channel classification,the accuracy of the LM network model and the traditional BP network model was 93.5%,higher than the accuracy(92.4%)of the traditional CART decision tree model.For classifying 156 test samples,the overall accuracy of the LM network model and the traditional BP network model was 96.2%,higher than the accuracy(94.8)of the traditional CART decision-tree model.For classification of the normal channels,the accuracy of all three models reached 100%.The accuracy of the LM network model and the traditional BP network model was 8%higher than the traditional CART decision-tree model for classifying defect channels.Analysis of the ROC curves of the three models for the test samples showed that the LM neural network model worked better in the classification lines for both normal channels and the defect channels.【Conclusion】Of all three models tested,the LM neural network model worked best and can be used to detect operational status of the channel system in irrigation districts.Its accuracy in detecting defect channels was 80.95%.
作者
赵钟声
许景辉
王雷
王一琛
ZHAO Zhongsheng;XU Jinghui;WANG Lei;WANG Yichen(Northwest A&F University Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas,Yangling 712100,China;College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling 712100,China;College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China)
出处
《灌溉排水学报》
CSCD
北大核心
2020年第11期130-136,共7页
Journal of Irrigation and Drainage
基金
陕西水利科技计划项目(2014slkj-18)
科技部国家重点研发计划课题(2017YFC0403203)。
关键词
数据挖掘
灌区渠道
神经网络
模型
data mining
irrigation district channels
neural networks
model
