Evaluation and Analysis of Goodness of Fit for Water Quality Parameters using Linear Regression through the Internet of Things (IoT) based Water Quality Monitoring System

2021 ◽  
pp. 1-1
Author(s):  
Harish H. Kenchannavar ◽  
Prasad M. Pujar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Internet Of Things ◽  
Monitoring System ◽  
Goodness Of Fit ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Water Quality Monitoring System Using IOT

2020 ◽  
Vol 3 (1) ◽  
pp. 155-164
Author(s):  
Suruchi Pokhrel ◽  
Anisha Pant ◽  
Ritisha Gautam ◽  
Dinesh Baniya Kshatri
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Monitoring System ◽  
Distribution Systems ◽  
Water Quality Monitoring ◽  
End Users ◽  
Quality Monitoring ◽  
The Internet ◽  
Physical Parameters ◽  
The Internet Of Things

Water pollution is one of the growing issues in a developing country like Nepal. In the present scenario, we are usually thoughtlessly trusting the drinking water suppliers with our health. Even though the water is purified as well as checked in the central distribution systems, the supplier, along with the general public is unaware of the water quality that reaches the end-users. By focusing on these above issues, we propose a low-cost monitoring system that can monitor water quality such as pH (potential of Hydrogen) and conductivity on a timely basis using the Internet of Things. The water quality monitoring sensors sense the necessary physical parameters and convert them into equivalent electrical form, i.e. by providing certain voltage as an output corresponding to the respective physical quantity. This value is mapped to the respective water quality measure and is stored in a database through the microcontroller using the Internet of Things. This aids the suppliers to centralize the regular monitoring of water from various locations as well as the supply pure water to the end-users.

scholarly journals Designing a Mobile Application for Pool Water Quality Monitoring Based on the Internet of Things

2021 ◽  
Vol 9 (1) ◽  
pp. 47-55
Author(s):  
Yohanes Anton Nugroho ◽  
Muhammad Fitra Pratama
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Fish Mortality ◽  
The Internet ◽  
Monitoring Equipment ◽  
Pool Water ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Changes in temperature, pH, and turbidity in concrete fish ponds greatly impact to the fish survival. Initial observations showed that among 3.067 fish seeds, 1.633 fish (53%) died and only 1.434 fish (47%) was successfully harvested. The application of water quality monitoring devices from concrete pools designed based on the Internet of Things technology has been tested. The monitoring equipment will not function optimally without an application that functions to receive monitoring data and then take action. Pool water quality monitoring equipment connected to the cloud using a GSM network connection. The recorded data is then displayed on the water quality monitoring application that designed using the Android operating system. Application design is developed using a User-Centered Design approach, where the design process was carried out by considering several variables: ease for use, clarity of information delivery, the fulfillment of needs, and appearance. Based on the results of the design evaluation, weaknesses can be determined, namely, difficulty to find the search menu for click history data, find the refresh button, read the results of searching for historical data, and read data in tables and graphs. Based on this, further improvements can be made to improve the application being made. The monitoring equipment is expected to provide information to pond managers to immediately take action if changing in pH and temperature beyond the limit so that the fish mortality rate can be minimized.

scholarly journals Internet of Things for Water Quality Monitoring using Estimate Dissolved Oxygen and Free Ammonia

2021 ◽  
Author(s):  
Rheza Shandikri ◽  
Bayu Erfianto
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Dissolved Oxygen ◽  
Fish Farming ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
The Internet ◽  
Measuring Devices ◽  
Small Scales ◽  
The Internet Of Things

In fish farming or aquaponics, one of the problems that are often encountered is water quality. Several parameters that must be monitored are ammonia, temperature, pH, and dissolved oxygen. There are available measuring devices for oxygen and ammonia levels in the market, but the price of the tool is not suitable for small scales. This study uses the Emerson formula and the Benson-Krause formula to determine ammonia and dissolved oxygen value. In this study, the two values were measured using RMSE, MAE, and MAPE against NH3 and Dissolved Oxygen values from Seneye. The output of this research is the level of water quality using Fuzzy logic and implementing the Internet of things to minimize human intervention with objects

scholarly journals Water monitoring and analytic based thingspeak

2020 ◽  
Vol 10 (4) ◽  
pp. 3588
Author(s):  
Abbas Hussien Miry ◽  
Gregor Alexander Aramice
Keyword(s):  
Water Quality ◽  
Normal Values ◽  
Water Quality Monitoring ◽  
Lag Time ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Proposed Model ◽  
Response To Water ◽  
Matlab Programming ◽  
The Internet Of Things

Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

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