Water-bloom short-time predicting system of Beijing based on neural network

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (28): 243-245.

• 工程与应用 • Previous Articles Next Articles

Water-bloom short-time predicting system of Beijing based on neural network

LIU Zai-wen¹,YANG Bin¹,HUANG Zhen-fang²,ZHANG Yan³

1.School of Information Engineering,Beijing Technology and Business University,Beijing 100037,China
2.Beijing Water Authority,Beijing 100038,China
3.Qingdao Huanghai College,Qingdao,Shandong 266472,China

Received:1900-01-01 Revised:1900-01-01 Online:2007-10-01 Published:2007-10-01
Contact: LIU Zai-wen

基于神经网络的北京市水体水华短期预报系统

刘载文¹,杨斌¹,黄振芳²,张艳³

1.北京工商大学信息工程学院,北京 100037
2.北京市水文总站,北京 100038
3.青岛黄海学院,山东青岛 266472

通讯作者: 刘载文

Abstract

Abstract: A water-bloom short-time predicting system of Beijing CHANGHE water system is founded with ameliorated BP neural network,in which content of chlorophyll,phosphor,ratio of nitrogen and phosphor,conductance and temperature of water are chose as the inputs of model and the target is to predict the contents of chlorophyll after 1 day,3 days and 5 days.The precisions of three periods,which the system get,reached separately 97.2%,94%,88.3%,and the system has good universality.Compared with other intelligent methods,BP neural network is simple,convenient and practicality,and has good appliance.

Key words: water-bloom, neural network, predicting, chlorophyll, beijing

摘要： 采用算法改进型的BP神经网络,选择叶绿素含量、磷、氮磷比、电导率和水温五个参数作为模型输入,以预测1日、3日和5日后的叶绿素含量为目标,构建了北京市长河水系水华短期预报系统。该系统三个周期的预测精度分别达到了97.2%、94%、88.3%,并且具有较好的泛化能力。相比于其它智能算法,BP神经网络结构简单、方便实用,仍然具有很强的应用性。

关键词: 水华, 神经网络, 预报, 叶绿素, 北京

LIU Zai-wen¹,YANG Bin¹,HUANG Zhen-fang²,ZHANG Yan³. Water-bloom short-time predicting system of Beijing based on neural network[J]. Computer Engineering and Applications, 2007, 43(28): 243-245.

刘载文¹,杨斌¹,黄振芳²,张艳³. 基于神经网络的北京市水体水华短期预报系统[J]. 计算机工程与应用, 2007, 43(28): 243-245.

[1]	MOU Qingping, ZHANG Ying, ZHANG Dongbo, WANG Xinjie, YANG Zhiqiao. Research on Visual Tracking Algorithm and Application of Target Loss Discrimination Mechanism [J]. Computer Engineering and Applications, 2021, 57(9): 140-147.
[2]	BAO Zhiqiang, XING Yu, LYU Shaoqing, HUANG Qiongdan. Improved YOLO V2 6D Object Pose Estimation Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 148-153.
[3]	WANG Lin, CHAI Jiangyun. Research on Deep Neural Network in Multi-scene Vehicle Attribute Recognition [J]. Computer Engineering and Applications, 2021, 57(9): 162-167.
[4]	HUANG Dongyi, YANG Bing, WU Zihao, KUANG Jiayi, YAN Zeming. Spatio-Temporal Fully Connected Convolutional Neural Networks for Citywide Cellular Prediction [J]. Computer Engineering and Applications, 2021, 57(9): 168-175.
[5]	ZHAO Zhiyan, YANG Hua, HU Zhiwei, YU Haiping. Identification Model of Pests on Yuluxiang Pear Leaves Based on TACNN [J]. Computer Engineering and Applications, 2021, 57(9): 176-181.
[6]	ZHOU Lungang, SUN Yifeng, WANG Kun, WU Jiang, HUANG Weigui, LI Binglong. End to End Object Recognition Algorithm for Multi-attributes of Multi-values [J]. Computer Engineering and Applications, 2021, 57(9): 182-190.
[7]	ZHANG Cheng, DAI Junfeng, XIONG Wenxin. Improved Handwritten Date Recognition in Scanned Documents Combined with LeNet-5 [J]. Computer Engineering and Applications, 2021, 57(9): 207-211.
[8]	MA Zhexu, YANG Feng, QIAO Xu. Intelligent Detection Method of Railway Subgrade Defect [J]. Computer Engineering and Applications, 2021, 57(9): 272-278.
[9]	XU Hao, ZHANG Kai, TIAN Yingjie, CHONG Faguang, WANG Zichao. Review of Deep Neural Network-Based Image Caption [J]. Computer Engineering and Applications, 2021, 57(9): 9-22.
[10]	RAN Rong, XU Xinghua, QIU Shaohua, CUI Xiaopeng, OUYANG Bin. Review of Crack Detection Methods Based on Deep Convolutional Neural Networks [J]. Computer Engineering and Applications, 2021, 57(9): 23-35.
[11]	JIANG Bin, ZHONG Rui, ZHANG Qiuwen, ZHANG Huanlong. Survey of Non-frontal Facial Expression Recognition by Using Deep Learning Methods [J]. Computer Engineering and Applications, 2021, 57(8): 48-61.
[12]	LI Zhenxiao, SUN Wei, LIU Mingming, ZHENG Lili, CHEN Shaoying. Research on Vehicle Detection and Tracking Algorithms in Traffic Monitoring Scenes [J]. Computer Engineering and Applications, 2021, 57(8): 103-111.
[13]	ZHANG Yue, HUANG Yourui, LIU Pengkun. Research on Multi-resolution Human Pose Estimation with Attention Mechanism [J]. Computer Engineering and Applications, 2021, 57(8): 126-132.
[14]	LI Xianguo, FENG Xinxin, LI Jianxiong. Sigle Image Super-Resolution Reconstruction Based on Multi-scale Residual Network [J]. Computer Engineering and Applications, 2021, 57(7): 215-221.
[15]	ZHAI Zhengli, LI Penghui, FENG Shu. Research Overview of Adversarial Attacks on Graphs [J]. Computer Engineering and Applications, 2021, 57(7): 14-21.

Water-bloom short-time predicting system of Beijing based on neural network

基于神经网络的北京市水体水华短期预报系统

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics