Final project | PENTACODE

A SURVEY OF WATER QUALITY MEASUREMENT SENSORS Water is essential for the survival of all life. Humans depend on water to fulfill a variety of needs and wants. These include water for drinking, health and sanitation, recreation and as part of spiritual and cultural traditions. Water quality is measure of suitability of water for particular use. It depends on various physical, chemical and biological parameters. Generally measured water quality parameters are temperature, turbidity, pH, conductivity, dissolved oxygen (DO) and total dissolved solid (TDS).These parameters are measured routinely in order to maintain the good water quality. Temperature: Water temperature is one of the five important factors for water quality testing. It controls the rate of metabolic and reproductive activities and hence aquatic life cycle. If water temperature increases, decreases or fluctuate, these activities may speed up, slow down or stop. Thermoelectric power and heat resistance tempera...

pH, Turbidity and temperature measurement This project is carried out for the measurement of quality of water for the Metropolia UAS using sensors and microcontroller. Water is indispensable for the living beings, and clean water is critical because of the modern world pollutions into the water in different forms. This project focuses on how we can measure and analyse the quality of water. This project focuses on measuring pH, Turbidity and Temperature of water. These three factors are considered because pH determines either the water be acidic or basic, Turbidity helps to determine the amount of solid particle, Where as these values may slightly differ with the change in temperature. The need for maintaining the quality of water is essential since the water is used or con-sumed by living beings in many different ways. Whether it is for a human being or aquatic plants or fishes, the properly maintained water is crucial for the proper sustainability. The measurement was...

In 2014, Malek and his team conducted a study on two lakes named Chini and Bera. They collected samples from 2005 to 2009. The data sample consisted of 11 parameters which were used to predicate Dissolved Oxygen  concentration. The Dissolved Oxygen concentration was dichotomized into three different levels such as, High, Medium and Low. They ranked the input parameters and they used forward selection method to determine the optimum parameters that yield the lowest errors and highest accuracy. The initial results showed that pH, temperature and conductivity significantly affect the prediction of Dissolved Oxygen. Then, they applied SVM model using the Anova kernel with those parameters yielded 74% accuracy rate. They concluded that using dichotomized value of Dissolved Oxygen yields higher prediction accuracy than using precise Dissolved Oxygen  value and ANOVA is the most appropriate kernel to obtain the highest accuracy.

At Terengganu River, Malaysia, a study was conducted to predict Dissolved Oxygen using SVM. They conducted the study for two different stations using the five parameters such as, pH, temperature, electrical conductivity and Nitrate and Ammonia Nitrogen. They used SVM with its non-linear and stochastic modelling proficiencies. The performance of the model was evaluated using three statistical indexed such as, Mean Squared Error (MSE), Coefficient of Efficiency (CE) and coefficient of Correlation (CC). They concluded that SVM can give robust and precise result and able to give fairly accurate predictions. It can also help in optimizing the water quality monitoring programs.

Automated Water Quality Survey and Evaluation Using an IoT Platform with Mobile Sensor Nodes Teng Li1,*, Min Xia1, Jiahong Chen1, Yuanjie Zhao2and Clarence de Silva1 Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Faculty of Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Received: 31 May 2017; Accepted: 24 July 2017; Published: 28 July 2017 In this survey it can understand that they have used Mobile Sensor Nodes here. Various water parameters such as flow rate, temperature, air pressure, pH value, dissolved oxygen, electrical conductivity, oxidation-reduction potential, nitrogen, phosphate, organic matter, microorganisms, can be monitored through automated sensing. In this developed platform, five sensors have implemented in each Mobile Sensor Network to measure five representative parameters. They are as Temperature sensor , pH Value sensor ,Dissolved Oxygen (DO) sensor ,   Electrica...

Wireless Sensor Networks: A survey monitoring water quality Electrical, Computer and Telecommunication Engineering Department of Botswana International University of Science and Technology Palapye Botswana 28 th April 2016   (07) With the development of the world and the changes occurring in the nature, the water resources are getting highly degraded. So the researchers have to do many investigations for monitoring the quality of water for various usages. With regarding the above survey, they have conducted the studies examining the water quality by using wireless sensor networks. These networks were relatively affordable and allowed measurements to be taken remotely in real time and with minimum human intervention. As they monitored air, forest and climatology, they were able to use wireless sensor networks. But in our project we are using wired sensor networks because we need high s...

                                                         This research work aims to compare the performance of the data mining algorithms with soil limitations and soil conditions in respect of the following characteristics: Acidity, Alkalinity and sodality, Salinity, Phosphorus fixation, cracking and swelling properties, Depth, Soil density and Nutrient content. This overall process of finding useful knowledge in raw data involves the following steps: 1.      Developing an understanding of the application domain 2.      Creating a target dataset based on an intelligent way of selecting data by focusing on a subset of variables or data samples 3.      Data cleaning and pre–processing 4.      Data reduction and projection 5.      Choo...