Ecological water quality assessment system based on BP neural network

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>

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Water Quality Assessment Using Artificial Neural Network

2008 International Conference on Computer Science and Software Engineering ◽

10.1109/csse.2008.411 ◽

2008 ◽

Cited By ~ 1

Author(s):

Ma Huiqun ◽

Liu Ling

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Quality Assessment ◽

Water Quality Assessment ◽

Artificial Neural

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Geospatial water quality assessment system for the Sg. Buloh river basin in Malaysia

International Journal of Water ◽

10.1504/ijw.2014.065795 ◽

2014 ◽

Vol 8 (4) ◽

pp. 401

Author(s):

M.K. Rowshon ◽

M.M. Mbaruk ◽

Martin J. Marriott ◽

M.S.M. Amin ◽

Amimul Ahsan ◽

...

Keyword(s):

Water Quality ◽

Quality Assessment ◽

River Basin ◽

Water Quality Assessment ◽

Assessment System

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Establishment and Application of Water Quality Assessment Model for Jiaozhou Bay Basin

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.1165 ◽

2012 ◽

Vol 518-523 ◽

pp. 1165-1170 ◽

Cited By ~ 3

Author(s):

Chao Liu ◽

Hui He ◽

Xiao Hui Tan ◽

Ai Li Gao ◽

Song Xue

Keyword(s):

Water Quality ◽

Quality Assessment ◽

Bp Neural Network ◽

Jiaozhou Bay ◽

Water Quality Assessment ◽

Training Sample ◽

Assessment Method ◽

Assessment Model ◽

Water Quality Status ◽

Effectiveness Of Training

In this paper, a comprehensive water quality assessment model for the seagoing rivers of the Jiaozhou Bay basin was established based on a BP neural network. In the situation investigation, a list of main assessment indexes was selected, comprising COD, permanganate, DO, ammonia, volatile hydroxybenzene and mineral oil. Then Environmental Quality Standards for Surface Water was used as the training sample and comprehensive assessment was conducted for the rivers. In Comparison with results from the conventional single-factor assessment method, this model not only responded to the comprehensive river water quality status, but also improved the speed and effectiveness of training, saving time and increasing accuracy of the assessment model through a series of design optimizations.

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