Bibliographic Details
| Title: |
Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network |
| Authors: |
Hajar Feizi, Mohammad Taghi Sattari, Mohammad Mosaferi |
| Source: |
محیط زیست و مهندسی آب, Vol 9, Iss 2, Pp 211-224 (2023) |
| Publisher Information: |
Iranian Rainwater Catchment Systems Association, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Environmental sciences
LCC:Water supply for domestic and industrial purposes |
| Subject Terms: |
deep learning, image, laboratory, python software, water quality, Environmental sciences, GE1-350, Water supply for domestic and industrial purposes, TD201-500 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
Persian |
| ISSN: |
2476-3683 |
| Relation: |
http://www.jewe.ir/article_164818_8774ae7d9faa1d2d33c2c952e343a863.pdf; https://doaj.org/toc/2476-3683 |
| DOI: |
10.22034/ewe.2022.349535.1795 |
| Access URL: |
https://doaj.org/article/a17f2067c63742e394146fb79b0b2998 |
| Accession Number: |
edsdoj.17f2067c63742e394146fb79b0b2998 |
| Database: |
Directory of Open Access Journals |
