scholarly journals Water Pollution Prediction Based on Deep Belief Network in Big Data of Water Environment Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li Liang
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Big Data ◽  
Prediction Model ◽  
Sewage Treatment ◽  
Water Environment ◽  
Oxygen Demand ◽  
Environment Monitoring ◽  
Water Quality Prediction ◽  
Pollution Prediction

Aiming at the problems that the traditional water quality prediction model is generally not high in prediction accuracy and robustness, a water pollution prediction using deep learning in water environment monitoring big data is proposed. Objective. To optimize and improve the prediction accuracy of the water quality prediction model. Firstly, in the water environment monitoring system, the Internet of Things big data technology is used to accurately sense and monitor the real-time data of sewage treatment equipment and sewage quality. Then, the deep belief network (DBN) is used to build the water pollution prediction model, and the collected sewage treatment data is analyzed to predict the water quality status. Finally, particle swarm optimization algorithm is used to dynamically optimize the number of hidden layer neural units and learning rate in the DBN prediction model, which makes the prediction results more scientific and accurate. Based on the sampling data of Shanghai Jinze Reservoir, the proposed model is experimentally analyzed. The results show that the probability of accurate location of the pollution source is not less than 70%. And under the two indicators of chemical oxygen demand and biological oxygen demand, the root mean square error and correlation coefficient are 3.073, 0.9892 and 1.958, 0.9565, respectively, which are better than other comparison models.

Water Quality Autoregression Prediction Model Based on Markov-Switching Theory Using Crow Search Algorithm

2020 ◽  
Author(s):  
Chunlei Liu ◽  
Chengzhong Pan ◽  
Yawen Chang ◽  
Mingjie Luo
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Time Series ◽  
Prediction Model ◽  
Search Algorithm ◽  
Markov Switching ◽  
Information Criterion ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Fifth Order

<p>Water quality prediction is an important technical means for preventing and controlling water pollution and is crucial in the formulation of reasonable water pollution prevention and control measures. The time series structure of natural water quality is complex and heteroscedastic, so it is difficult for the traditional prediction model to reflect the actual situation well. Hence, Markov-switching (MS) theory is applied to a water quality autoregression (AR) prediction model (MSAR) in this paper. Further, MSAR is improved by introducing the crow search algorithm to obtain model parameters (CSA-MSAR). Then existing water quality time series for COD<sub>Mn</sub> was selected as the data for the CSA-MSAR model after a normality test and the Box–Cox normality transformation. The results show that the CSA-MSAR model for COD<sub>Mn</sub> with (s, p) values of (3, 5) has the best performance. The improvement degree for selection criteria compared with AR model is as follows: Akaike information criterion for MSAR is 32.020% and 31.611% for CSA-MSAR; Bayesian information criterion for MSAR is 10.632% and 13.464% for CSA-MSAR; likelihood value for MSAR is 40.016% and 40.801% for CSA-MSAR; C for MSAR is 63.559% and 64.968% for CSA-MSAR. Moreover, the results show that the average prediction precision of the first- to fifth-order prediction is raised by 89.016% for MSAR and 89.340% for CSA-MSAR compared with AR, indicating that the introduction of MS makes the CSA-MSAR and MSAR models conform to the smoothness of the mean and variance in each state. The results also indicate that the introduction of CSA into the maximum likelihood estimation to obtain the parameters raise the model prediction precision (the average prediction precision of CSA-MSAR is higher than MSAR by 5.231% excluding the fifth-order prediction) and the CSA-MSAR model is scientifically valid and reasonable for water quality prediction.</p>

scholarly journals Surveying causes of surface water pollution at Sao River, Binh Thuy district, Can Tho city, Vietnam

2021 ◽  
Vol 9 (2) ◽  
pp. 103-110
Author(s):  
Nguyen Thanh Giao
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Surface Water ◽  
Solid Waste ◽  
Biological Activities ◽  
Water Environment ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Domestic Wastewater ◽  
Wastewater Management

Surface water sources play an important role in human and biological activities and the socio-economic development of the region. Therefore, the assessment of water quality and determination of the causes of water pollution in Sao river is essential for good management of the surface water environment. The study was conducted from July to December 2020. Water samples were collected at the time of low tide to evaluate the water quality indicators of temperature, pH, conductivity (EC), dissolved oxygen (DO), biological oxygen demand (BOD), total suspended solids (TSS), ammonium (N-NH4+), orthophosphate (P-PO43-) and coliform. The source of pollution was determined by direct interviews with households living near Sao river. The results showed that surface water quality in Sao river had signs of organic pollution and microbiological pollution due to BOD, TSS, N-NH4+, P-PO43-, coliform exceeded the allowable limits of National Technical regulation on surface water quality (QCVN 08-MT:2015/BTNMT, column A1). The results of the interview revealed that 70% of respondents said that water was seriously polluted and the main sources of pollution were domestic solid waste and domestic wastewater. Therefore, to improve surface water quality in Sao river, solid waste and wastewater management is urgently required. It is necessary to promote the monitoring and management of water quality with the participation of local authorities and communities.

scholarly journals Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2392
Author(s):  
Woo Suk Jung ◽  
Sung Eun Kim ◽  
Young Do Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Oxygen Demand ◽  
Meteorological Station ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Artificial Neural

We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term.

scholarly journals Water quality in relation to land use in the Junshan Lake watershed and water quality predictions

2021 ◽  
Author(s):  
Jiangang Lu ◽  
Haisheng Cai ◽  
Xueling Zhang ◽  
Yanmei Fu
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Use Changes ◽  
Water Environment ◽  
Oxygen Demand ◽  
Quality Standard ◽  
Lake Basin ◽  
Pollution Load ◽  
Water Quality Prediction

Abstract Changes in human-dominated spatial patterns of land use are the main driving factors of water quality evolution in watersheds, and the quantitative impact of land use changes on water quality is currently a focus of lake ecology research. Using the Junshan Lake Basin as a study area, this paper quantitatively analyzes the response relationships between the water quality parameters, land use, and socio-economic factors in the study area from 2005 to 2019 and predicts the water quality in 2035 based on land and space planning scenarios. The results show the following. (1) The land use structure of the Junshan Lake Basin has changed significantly over the last 15 years. The basic trend is an increase in settlement and wetland areas in the basin and a decrease in water, cropland, forest, and grassland areas. (2) Settlement areas play the role of a ‘source’ for the total phosphorus (TP) and ammonium-nitrogen (NH3-N) pollution load, and cropland areas play the role of a ‘sink’ for the TP, NH3-N, and chemical oxygen demand (CODMn) pollution load. (3) The main land use type in the Junshan Lake Basin is cropland, which accounts for more than 40% of the total, and the water quality in the lake is affected not only by non-point source pollution but also by the regional Gross Domestic Product (GDP), total population, and per capita disposable income. According to the water quality prediction and analysis, the concentrations of TN and TP in Junshan Lake will meet the Class IV water quality standard in 2035, and the concentrations of dissolved oxygen (DO) and CODMn will meet the Class II standard. This study is significant for the management and control of the water environment in the Junshan Lake Basin.

A Software Platform for Water Environment Monitoring

2014 ◽  
Vol 898 ◽  
pp. 743-746
Author(s):  
Chun Long Li ◽  
Xian Xiang Chen ◽  
Zhen Fang ◽  
Jian Hua Tong ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Water Quality ◽  
Low Cost ◽  
Water Environment ◽  
Smart Phone ◽  
Oxygen Demand ◽  
Quality Data ◽  
Environment Monitoring ◽  
Software Platform ◽  
Water Quality Data ◽  
Lightweight Framework

This paper describes a software platform for water environment monitoring. The main monitored parameters are temperature, turbidity, PH, dissolved oxygen, chemical oxygen demand (COD), total phosphorus, total nitrogen, nitrogen ammonia (NH) and heavy metal such as Pb, Zn and Cu etc. This platform was designed using java language and java web technology, which are widely used in many software platforms including water environment monitoring. Low cost and lightweight framework are the major aspects of the software platform because free software (Tomcat and MySQL) and SSH framework are adopted in this software platform. People can view water quality data in a computer or a smart phone browser in the form of table and chart. The water quality data transmitted from General Packet Radio Service (GPRS) wireless network are stored into the MySQL database automatically once the software platform is started. Data collected by this platform is real-time, once a record is out of limits, a message will be sent to mobile phone. Through data collected, environment protection administrators can predict and get the conclusion whether the water is polluted or not.

MEASURES FOR ENVIRONMENT CONSERVATION IN ENCLOSED COASTAL SEAS

2017 ◽  
Author(s):  
Keizo Negi ◽  
Keizo Negi ◽  
Takuya Ishikawa ◽  
Takuya Ishikawa ◽  
Kenichiro Iba ◽  
...  
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Water Mass ◽  
Sea Water ◽  
Red Tide ◽  
Water Environment ◽  
Quality Standard ◽  
Tokyo Bay ◽  
View Point ◽  
High Concentration

Japan experienced serious water pollution during the period of high economic growth in 1960s. It was also the period that we had such damages to human health, fishery and living conditions due to red tide as much of chemicals, organic materials and the like flowing into the seas along the growing population and industries in the coastal areas. Notable in those days was the issues of environment conservation in the enclosed coastal seas where pollutants were prone to accumulate inside due to low level of water circulation, resulting in the issues including red tide and oxygen-deficient water mass. In responding to these issues, we implemented countermeasures like effluent control with the Water Pollution Control Law and improvement/expansion of sewage facilities. In the extensive enclosed coastal seas of Tokyo Bay, Ise Bay and the Seto Inland Sea, the three areas of high concentration of population, we implemented water quality total reduction in seven terms from 1979, reducing the total quantities of pollutant load of COD, TN and TP. Sea water quality hence has been on an improvement trend as a whole along the steady reduction of pollutants from the land. We however recognize that there are differences in improvement by sea area such as red tide and oxygen-deficient water mass continue to occur in some areas. Meanwhile, it has been pointed out that bio-diversity and bio-productivity should be secured through conservation/creation of tidal flats and seaweed beds in the view point of “Bountiful Sea” To work at these challenges, through the studies depending on the circumstances of the water environment in the enclosed coastal seas, we composed “The Policy of Desirable State of 8th TPLCS” in 2015. We have also added the sediment DO into the water quality standard related to the life-environmental items in view of the preservation of aquatic creatures in the enclosed water areas. Important from now on, along the Policy, is to proceed with necessary measures to improve water quality with good considerations of differences by area in the view point of “Beautiful and bountiful Sea”.

Study on Water Quality Improvement of Streams in Baoan District, Shenzhen City, China

2011 ◽  
Vol 347-353 ◽  
pp. 1902-1905
Author(s):  
Hua Li You
Keyword(s):  
Waste Water ◽  
Mathematical Model ◽  
Water Quality ◽  
Fresh Water ◽  
Water Discharge ◽  
Water Environment ◽  
Oxygen Demand ◽  
Polluted Water ◽  
Integrated Analysis ◽  
Shenzhen City

Water is the basis of natural resources and strategic economic resources.Deteriorated water environment of streams in Shenzhen city could have a great impact on ecological safety, people's health,and economic development.Based on the data of field observation and Remote sensing (RS) image,integrated analysis of the water degradation causes,and the changes of biochemical oxygen demand in five days(BOD5）concentration by mathematical model were carried out,which is on basis of percentage of waste water disposal,fresh water transformation,and harbor excavation, respectively.The results show that degradation causes of water quality were resulted from waste water discharge, harbor construction,and ecological environment damage, which could lead to slowly water exchange. Accordingly,the pollution can be easily to store in the bay,which result in water quality changes.The most important improved countermeasure is the control of waste water, which could be had a great effectiveness to decrease pollution.In addition, fresh water must be supplied after polluted water was cut off,which can be better improvement for water quality.This would be extreme improvement for hydrological dynamics due to 15m harbor excavation,which can significantly reduce BOD5 concentration.The innovation points of this paper is to mathematical model,which is based on the basis of qualitative analysis.

scholarly journals Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia

2018 ◽  
Vol 15 (10) ◽  
pp. 2258 ◽  
Author(s):  
Muhammad Mazhar Iqbal ◽  
Muhammad Shoaib ◽  
Hafiz Umar Farid ◽  
Jung Lyul Lee
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Water Quality Model ◽  
Oxygen Solubility ◽  
Quality Model ◽  
Spatial Profile ◽  
Climatic Regions ◽  
Model Predictions

A river water quality spatial profile has a diverse pattern of variation over different climatic regions. To comprehend this phenomenon, our study evaluated the spatial scale variation of the Water Quality Index (WQI). The study was carried out over four main climatic classes in Asia based on the Koppen-Geiger climate classification system: tropical, temperate, cold, and arid. The one-dimensional surface water quality model, QUAL2Kw was selected and compared for water quality simulations. Calibration and validation were separately performed for the model predictions over different climate classes. The accuracy of the water quality model was assessed using different statistical analyses. The spatial profile of WQI was calculated using model predictions based on dissolved oxygen (DO), biological oxygen demand (BOD), nitrate (NO3), and pH. The results showed that there is a smaller longitudinal variation of WQI in the cold climatic regions than other regions, which does not change the status of WQI. Streams from arid, temperate, and tropical climatic regions show a decreasing trend of DO with respect to the longitudinal profiles of main river flows. Since this study found that each climate zone has the different impact on DO dynamics such as reaeration rate, reoxygenation, and oxygen solubility. The outcomes obtained in this study are expected to provide the impetus for developing a strategy for the viable improvement of the water environment.

Water quality prediction model in open limestone systems

2020 ◽  
pp. 279-282
Author(s):  
Louis McDonald ◽  
Qingyun Sun ◽  
Jeffrey Skousen ◽  
Paul Ziemkiewicz
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Water Quality Prediction
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