Artificial Intelligence in River Quality Assessment

AbstractThe aim of this study was to evaluate the influence of environmental factors on the distribution of macroinvertebrate taxa in different sized lowland Lithuanian rivers. A secondary aim was to assess ecological river quality and to determine the most suitable biotic index. A final aim was to determine the most appropriate macroinvertebrate families for river quality assessment in Lithuania. Species composition and quantitative characteristics of benthic macroinvertebrate communities have been investigated using standard kick-sampling method by a standard hand net in 24 different river sites in spring. Physical and chemical environmental parameters were measured in the same study site as the macroinvertebrate sampling. A total of 186 taxa representing 66 families or higher taxonomic ranks of benthic macroinvertebrates have been identified. Water temperature and current velocity influenced the highest number of ivestigated families. Seven of the most tolerant and eleven of the most sensitive macroinvertebrate taxa for hydrochemical parameters related with organic pollution were determined. The DSFI method was founded to be the best index for assessment of ecological status for Lithuanian rivers until more accurate estimation method will be created.

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Artificial Intelligence Assists in Quality Assessment of Spirometry in Clinical Trials

10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4606 ◽

2021 ◽

Author(s):

E. Topole ◽

S. Biondaro ◽

I. Montagna ◽

S. Corre ◽

K. Ray ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Trials ◽

Quality Assessment

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Ground Water and River Quality Assessment for Some Heavy Metals and Physicochemical Parameters in Wukari Town, Taraba State, Nigeria

International Journal of Sciences ◽

10.18483/ijsci.1298 ◽

2017 ◽

Vol 3 (05) ◽

pp. 73-80 ◽

Cited By ~ 2

Author(s):

M. O. Aremu ◽

O. J. Oko ◽

C. Andrew

Keyword(s):

Heavy Metals ◽

Quality Assessment ◽

Ground Water ◽

Physicochemical Parameters ◽

River Quality

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PENERAPAN MODEL BERBASIS ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI KUALITAS AIR DI SUNGAI SUBAYANG KABUPATEN KAMPAR

Jurnal Ilmu Lingkungan ◽

10.31258/jil.14.1.p.18-28 ◽

2020 ◽

Vol 14 (1) ◽

pp. 18

Author(s):

Endang Agus Damanhuri ◽

Yusni Ikhwan Siregar ◽

Elfizar Elfizar

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Water Quality ◽

Artificial Neural Network ◽

Water Flow ◽

Mean Squared Error ◽

Human Life ◽

Water Quality Management ◽

River Quality ◽

Artificial Neural

Water quality management is very important to do, because water is an inseparable part of everyday human life. Monitoring water quality is a way to maintain the quality of waters, especially rivers. River quality monitoring that is usually done requires a lot of equipment, effort and expertise so that its application becomes expensive and complicated. Technology that is growing rapidly nowadays puts forward artificial intelligence as the backbone of the Industrial Revolution 4.0 which promises many conveniences for industry and government. One of artificial intelligence technology is machine learning with Artificial Neural Network algorithm which is commonly used to predict or forecast a future value. This artificial neural network can be used to help monitor river water quality. The objective of this research to develop Artificial Neural Networks (ANN) model to predict the paramater of river quality (DO, pH, turbidity, temperature, water flow, conductivity) in the Subayang River, Kampar Regency, using software Rapidminer. The performance of the ANN models was evaluated using root mean squared error (RMSE) and correlation squared (R2) as a second comparison, then the results of the testing implementation are compared with direct measurements in the field. With the RMSE values obtained in the test results of each parameter DO = 1.613, pH = 0.098, turbidity = 4.730, temperature = 0.493, water flow = 0.121 and conductivity = 0.909. The lower the RMSE level, the closer it is to Artificial Neural Network accuracy for value prediction.

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