scholarly journals Seasonality and Correlations between Water Quality Parameters in the Lower Danube at Chiciu for the Period 2010-2012

2020 ◽  
Vol 71 (2) ◽  
pp. 449-455
Author(s):  
Rodica-Mihaela Frincu ◽  
Cristian Omocea ◽  
Cerasela-Iuliana Eni ◽  
Eleonora-Mihaela Ungureanu ◽  
Olga Iulian
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Social Economic ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
The Other ◽  
Danube River ◽  
Quality Monitoring ◽  
The Social ◽  
Pollution Loads

The Danube River receives tributaries with different pollution loads, according to the social-economic characteristics of the adjacent regions. Water quality monitoring data from Chiciu, Calarasi county, Romania, for the three-year period (2010-2012), were analysed using statistical methods in order to identify correlations between parameters, as well as their evolution during the study period. The analysis has confirmedpositive correlations between nitrates and total nitrogen and between ortho-phosphates and total phosphorus. Negative correlations were found between water temperatures on one side and dissolved oxygen and nitrates on the other side. These parameters have a seasonal evolution, with high temperatures and low dissolved oxygen and nitrates levels during summer periods. Linear regression highlights decreasing nutrients pollution during the study period, which may be due to improved wastewater treatment along Danube tributaries.

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 Limnology of Merbok estuary, Kedah, Malaysia

2015 ◽  
Vol 41 (1) ◽  
pp. 13-19
Author(s):  
Kaniz Fatema ◽  
Wan Maznah Wan Omar ◽  
Mansor Mat Isa
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Quality Parameters ◽  
The Other ◽  
Wet Season ◽  
Hand Temperature ◽  
Do Concentration ◽  
Merbok Estuary

Water quality in three different stations of Merbok estuary was investigated limnologically from October, 2010 to September, 2011. Water temperature, transparency and total suspended solids (TSS) varied from 27.45 - 30.450C, 7.5 - 120 cm and 10 -140 mg/l, respectively. Dissolved Oxygen (DO) concentration ranged from 1.22-10.8 mg/l, while salinity ranged from 3.5-35.00 ppt. pH and conductivity ranged from 6.35 - 8.25 and 40 - 380 ?S/cm, respectively. Kruskal Wallis H test shows that water quality parameters were significantly different among the sampling months and stations (p<0.05). This study revealed that DO, salinity, conductivity and transparency were higher in wet season and TSS was higher in dry season. On the other hand, temperature and pH did not follow any seasonal trends.Bangladesh J. Zool. 41(1): 13-19, 2013

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.

Study on Prediction Model of Dissolved Oxygen about Water Quality Monitoring System Based on BP Neural Network

2014 ◽  
Vol 912-914 ◽  
pp. 1407-1411 ◽  
Author(s):  
Jing Xin Yan ◽  
Li Juan Yu ◽  
Wen Wu Mao ◽  
Shou Qi Cao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Monitoring System ◽  
Bp Neural Network ◽  
Water Quality Monitoring ◽  
High Water ◽  
Eriocheir Sinensis ◽  
Quality Monitoring

Eriocheir sinensis should cultivate in high water quality ponds, which is affected by many combined factors such as physics, chemistry, biology etc. Using the real-time water quality monitoring historical data to test one of the water quality indexes and predict this index in the next time has great significance. The dissolved oxygen is one of the most important indexes in aquaculture, such as in the Eriocheir sinensis pond. This paper established a dissolved oxygen prediction model of water quality monitoring system based on BP neural network. The forecast data which is predicted by the established model could fit the actual monitoring data very well.

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.

scholarly journals GSM Based Water Quality Monitoring System Using Arduino

2019 ◽  
Vol 6 (4) ◽  
pp. 22-26
Author(s):  
S Gokulanathan ◽  
P Manivasagam ◽  
N Prabu ◽  
T Venkatesh
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Rapid Development ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Vital Role ◽  
Quality Monitoring ◽  
Human Society ◽  
Power Efficient

This paper investigates about water quality monitoring system through a wireless sensor network. Due to the rapid development and urbanization, the quality of water is getting degrade over year by year, and it leads to water-borne diseases, and it creates a bad impact. Water plays a vital role in our human society and India 65% of the drinking water comes from underground sources, so it is mandatory to check the quality of the water. In this model used to test the water samples and through the data it analyses the quality of the water. This paper delivers a power efficient, effective solution in the domain of water quality monitoring it also provides an alarm to a remote user, if there is any deviation of water quality parameters.

scholarly journals Implementing Zigbee-based Wireless Sensor Network in the Design of Water Quality Monitoring System

2019 ◽  
Vol 8 (3) ◽  
pp. 6174-6179
Keyword(s):  
Water Quality ◽  
Wireless Sensor Network ◽  
Dissolved Oxygen ◽  
Water Level ◽  
Sensor Network ◽  
Water Quality Monitoring ◽  
Water Remediation ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Rf Transceiver

This study presents the design and development of a precision fishing technology utilized in water quality monitoring with phytoremediation system using a Zigbee-based Wireless Sensor Network. The system afforded a real-time water quality monitoring using multiple sensors spatially deployed. The sensor node implemented in the Wireless Sensor Network to perform data sensing utilities with the water quality parameters including the water temperature, pH level, water dissolved oxygen and the water level during high-tide and low-tide. During the development, a P89V51RD2 microcontroller, ZigBee module with IEEE 802.15.4 standard, and radio frequency (RF) transceiver were utilized. The developed precision fishing technology utilized the Internet of Things architecture. The IoT device layer includes the temperature sensor, pH sensor, dissolved oxygen sensor, and the water level sensor. Phytoremediation was also used as an alternative solution for soil and water remediation. Further studies using recent and advanced remote sensing technologies and IoT-based solutions can be developed to address issues in the primary sector of the economy.

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