Mohsin, Abu S.M.Rahman, NaveedNir, Riaz Uddin AhmedTithi, Saila HasanShupti, Baishakhi Rani Das2021-08-032021-08-0320212021-04ID 16321016ID 16221009ID 16121143ID 16321098http://hdl.handle.net/10361/14935Cataloged from PDF version of thesis.Includes bibliographical references (pages 76-79).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.In 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.99 pagesenBrac 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.Water quality monitoring(WQM)Water pollutionpHTDSTurbidityMachine learning and future predictionMachine learningInternet of thingsThesis