IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning
The significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinde...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/6/1458 |
| _version_ | 1797612173118144512 |
|---|---|
| author | Chandru Vignesh Chinnappan Alfred Daniel John William Surya Kalyan Chakravarthy Nidamanuri S. Jayalakshmi Ramadevi Bogani P. Thanapal Shahada Syed Boppudi Venkateswarlu Jafar Ali Ibrahim Syed Masood |
| author_facet | Chandru Vignesh Chinnappan Alfred Daniel John William Surya Kalyan Chakravarthy Nidamanuri S. Jayalakshmi Ramadevi Bogani P. Thanapal Shahada Syed Boppudi Venkateswarlu Jafar Ali Ibrahim Syed Masood |
| author_sort | Chandru Vignesh Chinnappan |
| collection | DOAJ |
| description | The significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinders socioeconomic growth. In addition, disinfectants such as chlorine and fluoride are used to remove pathogens, or disease-causing compounds, from water. After a substantial amount of chlorine has been added to water, its residue causes an issue. Since the proposed methodology is intended to offer a steady supply of drinkable water, its chlorine concentration must be checked in real-time. The suggested model continually updates the sensor hub regarding chlorine concentration measurements. In addition, these data are transmitted over a communication system for data analysis to analyze chlorine levels within the drinking water and residual chlorine percentage over time using a fuzzy set specifically using a decision tree algorithm. Additionally, a performance investigation of the proposed framework is undertaken to determine the efficiency of the existing model for predicting the quantity of chlorine substance employing metrics such as recall, accuracy, F-score, and ROC. Henceforth, the proposed model has substantially better precision than the existing techniques. |
| first_indexed | 2024-03-11T06:37:33Z |
| format | Article |
| id | doaj.art-1e61e21b6ef94e6dab2a6e4b07230bfa |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:37:33Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1e61e21b6ef94e6dab2a6e4b07230bfa2023-11-17T10:45:49ZengMDPI AGElectronics2079-92922023-03-01126145810.3390/electronics12061458IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine LearningChandru Vignesh Chinnappan0Alfred Daniel John William1Surya Kalyan Chakravarthy Nidamanuri2S. Jayalakshmi3Ramadevi Bogani4P. Thanapal5Shahada Syed6Boppudi Venkateswarlu7Jafar Ali Ibrahim Syed Masood8School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, IndiaCentre for Data Science, School of Computer Science and Engineering, QIS College of Engineering and Technology, Ongole 532274, IndiaDepartment of EEE, QIS College of Engineering and Technology, Ongole 532274, IndiaDepartment of CSE, QIS College of Engineering and Technology, Ongole 532274, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of IT, QIS College of Engineering and Technology, Ongole 532274, IndiaDepartment of EEE, QIS College of Engineering and Technology, Ongole 532274, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaThe significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinders socioeconomic growth. In addition, disinfectants such as chlorine and fluoride are used to remove pathogens, or disease-causing compounds, from water. After a substantial amount of chlorine has been added to water, its residue causes an issue. Since the proposed methodology is intended to offer a steady supply of drinkable water, its chlorine concentration must be checked in real-time. The suggested model continually updates the sensor hub regarding chlorine concentration measurements. In addition, these data are transmitted over a communication system for data analysis to analyze chlorine levels within the drinking water and residual chlorine percentage over time using a fuzzy set specifically using a decision tree algorithm. Additionally, a performance investigation of the proposed framework is undertaken to determine the efficiency of the existing model for predicting the quantity of chlorine substance employing metrics such as recall, accuracy, F-score, and ROC. Henceforth, the proposed model has substantially better precision than the existing techniques.https://www.mdpi.com/2079-9292/12/6/1458IoTmachine learningresidual chlorinewater quality assessmentfuzzy rulesdecision tree |
| spellingShingle | Chandru Vignesh Chinnappan Alfred Daniel John William Surya Kalyan Chakravarthy Nidamanuri S. Jayalakshmi Ramadevi Bogani P. Thanapal Shahada Syed Boppudi Venkateswarlu Jafar Ali Ibrahim Syed Masood IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning Electronics IoT machine learning residual chlorine water quality assessment fuzzy rules decision tree |
| title | IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning |
| title_full | IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning |
| title_fullStr | IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning |
| title_full_unstemmed | IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning |
| title_short | IoT-Enabled Chlorine Level Assessment and Prediction in Water Monitoring System Using Machine Learning |
| title_sort | iot enabled chlorine level assessment and prediction in water monitoring system using machine learning |
| topic | IoT machine learning residual chlorine water quality assessment fuzzy rules decision tree |
| url | https://www.mdpi.com/2079-9292/12/6/1458 |
| work_keys_str_mv | AT chandruvigneshchinnappan iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT alfreddanieljohnwilliam iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT suryakalyanchakravarthynidamanuri iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT sjayalakshmi iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT ramadevibogani iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT pthanapal iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT shahadasyed iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT boppudivenkateswarlu iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning AT jafaraliibrahimsyedmasood iotenabledchlorinelevelassessmentandpredictioninwatermonitoringsystemusingmachinelearning |