Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network
Among the important parameters in water quality, are the amount of turbidity and the depth of light penetration in water. One common way to determine water turbidity is to use a Secchi disk, but this method is time-consuming and expensive, so an alternative method should be considered. Deep learning...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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Iranian Rainwater Catchment Systems Association
2023-06-01
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| Series: | محیط زیست و مهندسی آب |
| Subjects: | |
| Online Access: | http://www.jewe.ir/article_164818_8774ae7d9faa1d2d33c2c952e343a863.pdf |
| _version_ | 1827252483954573312 |
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| author | Hajar Feizi Mohammad Taghi Sattari Mohammad Mosaferi |
| author_facet | Hajar Feizi Mohammad Taghi Sattari Mohammad Mosaferi |
| author_sort | Hajar Feizi |
| collection | DOAJ |
| description | Among the important parameters in water quality, are the amount of turbidity and the depth of light penetration in water. One common way to determine water turbidity is to use a Secchi disk, but this method is time-consuming and expensive, so an alternative method should be considered. Deep learning methods can play an important role in this field. The purpose of this study was to classify water quality based on turbidity and Secchi disk depth using a convolutional neural network method implemented in a Python programming environment. For this purpose, a simulated reservoir was used in the laboratory and the turbidity was increased step by step by increasing the clay in the reservoir water. Simultaneously with measuring the depth of the Secchi disk and water turbidity, the samples were imaged. These images were given to the convolutional neural network together with the obtained data. The results showed that the convolutional neural network with 300 epochs, can estimate the water quality class with 95% accuracy and 93% kappa statistic, and it has only a 5% error rate. |
| first_indexed | 2024-03-08T18:41:05Z |
| format | Article |
| id | doaj.art-a17f2067c63742e394146fb79b0b2998 |
| institution | Directory Open Access Journal |
| issn | 2476-3683 |
| language | fas |
| last_indexed | 2025-03-22T00:36:03Z |
| publishDate | 2023-06-01 |
| publisher | Iranian Rainwater Catchment Systems Association |
| record_format | Article |
| series | محیط زیست و مهندسی آب |
| spelling | doaj.art-a17f2067c63742e394146fb79b0b29982024-05-14T11:30:07ZfasIranian Rainwater Catchment Systems Associationمحیط زیست و مهندسی آب2476-36832023-06-019221122410.22034/ewe.2022.349535.1795164818Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural NetworkHajar Feizi0Mohammad Taghi Sattari1Mohammad Mosaferi2Ph.D Alumni, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, IranAssoc. Professor, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, IranProfessor, Department of Environmental Health Engineering, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, IranAmong the important parameters in water quality, are the amount of turbidity and the depth of light penetration in water. One common way to determine water turbidity is to use a Secchi disk, but this method is time-consuming and expensive, so an alternative method should be considered. Deep learning methods can play an important role in this field. The purpose of this study was to classify water quality based on turbidity and Secchi disk depth using a convolutional neural network method implemented in a Python programming environment. For this purpose, a simulated reservoir was used in the laboratory and the turbidity was increased step by step by increasing the clay in the reservoir water. Simultaneously with measuring the depth of the Secchi disk and water turbidity, the samples were imaged. These images were given to the convolutional neural network together with the obtained data. The results showed that the convolutional neural network with 300 epochs, can estimate the water quality class with 95% accuracy and 93% kappa statistic, and it has only a 5% error rate.http://www.jewe.ir/article_164818_8774ae7d9faa1d2d33c2c952e343a863.pdfdeep learningimagelaboratorypython softwarewater quality |
| spellingShingle | Hajar Feizi Mohammad Taghi Sattari Mohammad Mosaferi Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network محیط زیست و مهندسی آب deep learning image laboratory python software water quality |
| title | Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network |
| title_full | Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network |
| title_fullStr | Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network |
| title_full_unstemmed | Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network |
| title_short | Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network |
| title_sort | classification of water turbidity and depth of secchi disk using convolutional neural network |
| topic | deep learning image laboratory python software water quality |
| url | http://www.jewe.ir/article_164818_8774ae7d9faa1d2d33c2c952e343a863.pdf |
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