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dc.contributor.advisorMohsin, Abu S.M.
dc.contributor.authorRahman, Naveed
dc.contributor.authorNir, Riaz Uddin Ahmed
dc.contributor.authorTithi, Saila Hasan
dc.contributor.authorShupti, Baishakhi Rani Das
dc.date.accessioned2021-08-03T10:42:36Z
dc.date.available2021-08-03T10:42:36Z
dc.date.copyright2021
dc.date.issued2021-04
dc.identifier.otherID 16321016
dc.identifier.otherID 16221009
dc.identifier.otherID 16121143
dc.identifier.otherID 16321098
dc.identifier.urihttp://hdl.handle.net/10361/14935
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 76-79).
dc.description.abstractIn this project, we built and developed an IoT based system that can monitor the water quality of various places in real-time and provides future predictions regarding the water quality in each place. For this project, we developed a physical device that collects various data of water. This data was collected by various sensors built-in with this device. This data includes the water's pH level, turbidity level, TDS (Total dissolved Solid) level, Rain level, Sunlight level, etc. The physical device consists of a microcontroller that gathers these data and sent it to a secured website using a Wi-Fi module. This hardware device is wireless, and it is water-resistant as it was placed close to water sources. It consists of a big battery or solar panel to charge the device. Afterwards the hardware device sends data to the website, stores the information & collects data of water quality every day. Each day it collects data 2 times (once every 12 hours. We collected the data for 2 weeks and analyzed the data and performed future prediction. As the sample size was small therefore, we observed larger error rate, however the error was reduced increasing the number of data set. The proposed system will not only be helpful to observe the real-time monitoring of water quality but also to develop a better water management system for the local community.en_US
dc.description.statementofresponsibilityNaveed Rahman
dc.description.statementofresponsibilityRiaz Uddin Ahmed Nir
dc.description.statementofresponsibilitySaila Hasan Tithi
dc.description.statementofresponsibilityBaishakhi Rani Das Shupti
dc.format.extent99 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectWater quality monitoring(WQM)en_US
dc.subjectWater pollutionen_US
dc.subjectpHen_US
dc.subjectTDSen_US
dc.subjectTurbidityen_US
dc.subjectMachine learning and future predictionen_US
dc.subject.lcshMachine learning
dc.subject.lcshInternet of things
dc.titleWater quality monitoring using machine learning and Internet Of Things (IoT)en_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Electrical and Electronic Engineering, Brac University
dc.description.degreeB. Electrical and Electronic Engineering


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