基于GA-BP神经网络的施工区域水质预测及预警模型研究

doi:10.3969/j.issn.1674-0696.2020.12.17

重庆交通大学学报（自然科学版） ›› 2020, Vol. 39 ›› Issue (12): 106-110.DOI: 10.3969/j.issn.1674-0696.2020.12.17

• 港口航道·水利水电·资源环境 • 上一篇下一篇

基于GA-BP神经网络的施工区域水质预测及预警模型研究

李鑫鑫1，郑丹1，杨建喜2，蔡昊男1，曾维成3，岳锐强3

(1. 重庆交通大学河海学院，重庆 400074； 2. 重庆交通大学信息科学与工程学院，重庆 400074； 3. 云南武易高速公路建设指挥部，云南昆明 650200)

收稿日期:2019-04-28 修回日期:2019-12-10 出版日期:2020-12-18 发布日期:2020-12-18
作者简介:李鑫鑫（1985—），男，湖北宜昌人，副教授，博士，主要从事水质监测方面的研究。E-mail：305900503@qq.com 通信作者：蔡昊男（1996—），男，重庆人，硕士研究生，主要从事水质监测方面的研究。E-mail：405285477@qq.com
基金资助:
国家自然科学基金项目（51809025）

Water Quality Prediction and Early Warning Model of Construction Area Based on GA-BP Neural Network

LI Xinxin1, ZHENG Dan1, YANG Jianxi2, CAI Haonan1, ZENG Weicheng3, YUE Ruiqiang3

(1. School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2. School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 3. Yunnan Wuyi Expressway Construction Command, Kunming 650200, Yunnan, China)

Received:2019-04-28 Revised:2019-12-10 Online:2020-12-18 Published:2020-12-18
Supported by:

摘要/Abstract

摘要： 现有监测手段虽能实时反映水质状况，但无法对水质状况进行预测从而指导施工。基于此，以影响施工区域水质的因素为预测参数并基于监测数据，构建了GA-BP神经网络水质预测模型，并针对不同水质状况提出了应对措施。结合不同施工阶段高速公路施工区域水库水质状况，验证该预测方法和应对措施的有效性。结果表明：GA-BP神经网络模型不仅能准确有效地预测现场水质状况，而且还能基于水质预测结果和水质预警模型及早调整施工方案，减少工程建设对施工区域水环境的影响。

关键词: 水利工程, 施工, 水质, 神经网络, 监测系统, pH值

Abstract: The existing monitoring methods can reflect the water quality in real-time, but they cannot predict the water quality and guide the construction. Based on this, taking the factors affecting the water quality in the construction area as the prediction parameters and based on the monitoring data, the GA-BP neural network water quality prediction model was established, and the countermeasures for different water quality were also proposed. Combined with the water quality of reservoirs in expressway construction area at different construction stages, the effectiveness of the prediction method and countermeasures was verified. The results show that the proposed GA-BP neural network model can not only accurately and effectively predict the water quality of the site, but also adjust the construction scheme early based on the water quality prediction results and water quality early warning model, so as to reduce the impact of the project construction on the water environment in the construction area.

Key words: hydraulic engineering, construction, water quality, neutral network, monitoring system, pH

中图分类号:

P332.7

李鑫鑫1，郑丹1，杨建喜2，蔡昊男1，曾维成3，岳锐强3. 基于GA-BP神经网络的施工区域水质预测及预警模型研究[J]. 重庆交通大学学报（自然科学版）, 2020, 39(12): 106-110.

LI Xinxin1, ZHENG Dan1, YANG Jianxi2, CAI Haonan1, ZENG Weicheng3, YUE Ruiqiang3. Water Quality Prediction and Early Warning Model of Construction Area Based on GA-BP Neural Network[J]. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(12): 106-110.

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基于GA-BP神经网络的施工区域水质预测及预警模型研究

Water Quality Prediction and Early Warning Model of Construction Area Based on GA-BP Neural Network

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