A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2021
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10361-157362022-01-26T10:04:51Z A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning Hossain, Md. Mehedi Mridha, Md. Jahid Hasan Imran, Sazid Md. Wahid, SK Ayub Al Alam Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Effluent Treatment Plants (ETP) E-IoT Water monitoring Video classification Water Quality Index (WQI) RGB color analysis Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-56). Water is a vital element in our environment but day by day water pollution is increasing in an alarming rate in our country. In Bangladesh’s perspective, industries such as textile and ready-made garments (RMG) contribute to a massive amount of waste or effluent. Effluent treatment plant (ETP) are used to remove as much suspended solids from wastewater as possible before it gets back to the environment. However, according to a report published by the Environment and forests ministry, seven state-run factories don’t have any effluent treatment plant (ETP) to treat their waste before disposal. And also even the factories which has ETP do not always keep the ETP up and running because it consumes a lot of electricity. The purpose of our research is to establish a setup which will monitor the real-time quality of water outside the industries and inform us whether the ETP is turned on or not with the help of E-IoT and various classification algorithm. It will also predict the seasonal impact where the ETP might be turned off again and what will be the quality of water with the help of various machine learning and deep learning algorithms such as CNN, KNN and LSTM. We have also tracking the sensor value for monitoring and the ETP outlet with RGB color analysis. We have successfully achieved an accuracy of 99% for KNN, 97.5% for CNN and 94.9% forecasting model accuracy for LSTM. Md. Mehedi Hossain Md. Jahid Hasan Mridha Sazid Md. Imran SK Ayub Al Wahid B. Computer Science 2021-12-15T05:58:42Z 2021-12-15T05:58:42Z 2021 2021-01 Thesis ID 15201033) ID 16301052 ID 18201193 ID 19241023 http://hdl.handle.net/10361/15736 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 56 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Effluent Treatment Plants (ETP) E-IoT Water monitoring Video classification Water Quality Index (WQI) RGB color analysis Machine Learning |
| spellingShingle |
Effluent Treatment Plants (ETP) E-IoT Water monitoring Video classification Water Quality Index (WQI) RGB color analysis Machine Learning Hossain, Md. Mehedi Mridha, Md. Jahid Hasan Imran, Sazid Md. Wahid, SK Ayub Al A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam Md. Golam Rabiul |
| author_facet |
Alam Md. Golam Rabiul Hossain, Md. Mehedi Mridha, Md. Jahid Hasan Imran, Sazid Md. Wahid, SK Ayub Al |
| format |
Thesis |
| author |
Hossain, Md. Mehedi Mridha, Md. Jahid Hasan Imran, Sazid Md. Wahid, SK Ayub Al |
| author_sort |
Hossain, Md. Mehedi |
| title |
A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning |
| title_short |
A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning |
| title_full |
A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning |
| title_fullStr |
A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning |
| title_full_unstemmed |
A classification and prediction based approach for real-time ETP outlet monitoring through E-IoT and remote sensing using machine learning and deep learning |
| title_sort |
classification and prediction based approach for real-time etp outlet monitoring through e-iot and remote sensing using machine learning and deep learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15736 |
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