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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 05 | May 2023

p-ISSN: 2395-0072

www.irjet.net

WATER QUALITY MONITORING RC BOAT Dr. S. Vijayanand1, R. Gayathri2, Harini M3, Hithaishi U M4 1Assistant Professor, Sri Venkateswara College of Engineering, Sriperumbudur, TamilNadu

234Students of Electronics and Communication Engineering, Sri Venkateswara College of Engineering,

Sriperumbudur, TamilNadu ---------------------------------------------------------------------***--------------------------------------------------------------------2. LITERATURE REVIEW Abstract - Water pollution has become a major issue these days due to the dumping of industrial waste, harmful chemicals, and other pollutants. The quality of water plays a crucial role in the health of plants, animals, and human beings. Therefore, improved methods of water quality monitoring are required. The manual collection of water samples at various locations is a traditional method of water quality monitoring. The water quality is then characterized using laboratory analytical methods. So, here we design a solution for easy water quality checking of water bodies. This system measures the pH, Turbidity, Temperature, and humidity levels of water samples using the respective sensors, and based on the values, it gives information about whether the water is fresh or polluted and also what kind of aquatic flora and fauna can be grown. The interfaced sensors are placed on the boat and realtime data can be obtained once the boat moves over the water’s surface. This project is based on the Internet of Things, which will be controlled by the Arduino Nano using a mobile application. Accordingly, a propeller system to provide the forward propulsion and a servo motor arrangement to control the boat by moving left or right by means of a mobile device using a joystick module. The values of the sensors, the suggestions of aquatic flora and fauna, and the position of the boat can be viewed through the Adafruit webpage with the help of the Wi-Fi module.

Darshana R. Sarnaik, C. M. Jadhao, and Mhaske proposed a work on "Real Time Water Pollution Monitoring RC Boat Using IOT," a system that collects information from wireless sensors linked to a Raspberry Pi model B, processes it, and then compiles the information into a text file that is sent to IOT. A gateway is built on the Raspberry Pi 3 model B using the FTP (file transfer protocol) protocol for data transmission to the IOT. Cloud computing technology, which offers a private local server, is used to monitor the processed data on the internet. Separate IP addresses are offered by cloud computing technologies, enabling data monitoring via the internet from any location in the world. The system is made user-friendly by the introduction of a browser application that uses HTTP to retrieve that monitored data. Therefore, individuals from all around the world can access and monitor the data simply by utilizing this browser application. [1] Olasupo O. Ajayi, Hloniphani C. Maluleke, and Antoine B. Bagula Zaheed Gaffoor, Kevin C. Pietersen and Nebo Jovanovic worked on the process of collecting data on "WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes," which proposed a network architecture to gather information on water quality metrics in real-time and employs machine learning techniques to estimate the quality of water automatically for various purposes. This network is based on Lora and takes land topology into account. A partial mesh network topology is the most suitable network, according to simulation results. Here, three ML models have been used. They are Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). The water classification process took into account these algorithms, and the results showed that Linear Regression performed better for drinking water, while Support Vector Machine was better suited for irrigation water. A recursive feature elimination algorithm was then combined with these three Machine Learning models to identify which water parameter has the greatest influence on the classification efficiency of the respective model.[2]

Key Words: Arduino Nano, pH, turbidity, temperature, humidity, flora, fauna, Adafruit.

1.INTRODUCTION Water is the primary source of economic and social development. It is vital to maintain health, grow plants and crops and manage the environment. The demand for water is not only for human survival but also for aquatic flora and fauna, as they live in the habitat of water. Ensuring the safety of water is a challenge due to the excessive use of chemicals and fertilisers, most of which are man-made. Water pollution has been greatly exacerbated by modern advancements, agricultural pesticides, and the non-enforcement of laws, as well as by the rapid pace of industrialization and its greater impact on agricultural growth. Rainfall that is not distributed evenly can occasionally make the issue worse. When determining the quality of the water, individual practises are also crucial. Poor water quality may spread disease and inhibit socioeconomic progress.

Impact Factor value: 8.226

Mohamad Adhipramana, Rina Mardiati, and Edi Mulyana developed a robot system called "Remotely Operated Vehicle (ROV) Robot for Monitoring Quality of Water Based on IOT" to monitor the parameters of water quality based on the Internet of Things. The main hardware used here is the

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