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Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data
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作者 Wenwu Tan Jianjun Zhang +7 位作者 Xing Liu Jiang Wu yifu sheng Ke Xiao Li Wang Haijun Lin Guang Sun Peng Guo 《Journal on Big Data》 2023年第1期85-97,共13页
At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the p... At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality. 展开更多
关键词 Correlation analysis CLUSTER water quality predict water quality monitoring data
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Design and Analysis of a Water Quality Monitoring Data Service Platform 被引量:3
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作者 Jianjun Zhang yifu sheng +3 位作者 Weida Chen Haijun Lin Guang Sun Peng Guo 《Computers, Materials & Continua》 SCIE EI 2021年第1期389-405,共17页
Water is one of the basic resources for human survival.Water pollution monitoring and protection have been becoming a major problem for many countries all over the world.Most traditional water quality monitoring syste... Water is one of the basic resources for human survival.Water pollution monitoring and protection have been becoming a major problem for many countries all over the world.Most traditional water quality monitoring systems,however,generally focus only on water quality data collection,ignoring data analysis and data mining.In addition,some dirty data and data loss may occur due to power failures or transmission failures,further affecting data analysis and its application.In order to meet these needs,by using Internet of things,cloud computing,and big data technologies,we designed and implemented a water quality monitoring data intelligent service platform in C#and PHP language.The platform includes monitoring point addition,monitoring point map labeling,monitoring data uploading,monitoring data processing,early warning of exceeding the standard of monitoring indicators,and other functions modules.Using this platform,we can realize the automatic collection of water quality monitoring data,data cleaning,data analysis,intelligent early warning and early warning information push,and other functions.For better security and convenience,we deployed the system in the Tencent Cloud and tested it.The testing results showed that the data analysis platform could run well and will provide decision support for water resource protection. 展开更多
关键词 Water quality monitoring data analysis big data tencent cloud
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Application of Grey Model and Neural Network in Financial Revenue Forecast 被引量:2
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作者 yifu sheng Jianjun Zhang +4 位作者 Wenwu Tan Jiang Wu Haijun Lin Guang Sun Peng Guo 《Computers, Materials & Continua》 SCIE EI 2021年第12期4043-4059,共17页
There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good ... There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting. 展开更多
关键词 Fiscal revenue lasso regression gray prediction model BP neural network
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Visualization Research and Application of Water Quality Monitoring Data Based on ECharts 被引量:4
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作者 yifu sheng Weida Chen +2 位作者 Huan Wen Haijun Lin Jianjun Zhang 《Journal on Big Data》 2020年第1期1-8,共8页
Water resources are one of the basic resources for human survival,and water protection has been becoming a major problem for countries around the world.However,most of the traditional water quality monitoring research... Water resources are one of the basic resources for human survival,and water protection has been becoming a major problem for countries around the world.However,most of the traditional water quality monitoring research work is still concerned with the collection of water quality indicators,and ignored the analysis of water quality monitoring data and its value.In this paper,by adopting Laravel and AdminTE framework,we introduced how to design and implement a water quality data visualization platform based on Baidu ECharts.Through the deployed water quality sensor,the collected water quality indicator data is transmitted to the big data processing platform that deployed on Tencent Cloud in real time through the 4G network.The collected monitoring data is analyzed,and the processing result is visualized by Baidu ECharts.The test results showed that the designed system could run well and will provide decision support for water resource protection. 展开更多
关键词 Water quality monitoring echarts data visualization
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