Forecast optimal fluoride concentration data in drinking water according to the ambient temperature in Golestan, Iran

Zazouli, Mohammad Ali; Kalankesh, Laleh R.; Hasanpour, Maryam

doi:10.1002/tqem.21611

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…As far as a specific sensor is concerned, it has better performance in detecting specific pollutants, but its ability to detect other pollutants is weak. Usually this type of sensor is mainly aimed at pol-lutants such as heavy metal ions [4]. For general-purpose sensors, they are not designed for a specific type of pollution,so they have a more general detection ability for most pollution types [5].…”

Section: Introductionmentioning

confidence: 99%

Water Pollution Detection Using Video Surveillance and Deep Learning

Natake

2024

IJSREM

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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 than70%. 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.

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Section: Introductionmentioning

confidence: 99%

Water Pollution Detection Using Video Surveillance and Deep Learning

Natake

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…As far as a specific sensor is concerned, it has better performance in detecting specific pollutants, but its ability to detect other pollutants is weak. Usually this type of sensor is mainly aimed at pollutants such as heavy metal ions [4]. For general-purpose sensors, they are not designed for a specific type of pollution, so they have a more general detection ability for most pollution types [5].…”

Section: Introductionmentioning

confidence: 99%

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

2021

Scientific Programming

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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.

show abstract