Field Spectroscopy Measurements over Asprokremmos Dam in Cyprus Intended for Water Quality Monitoring

2012 ◽  
Vol 500 ◽  
pp. 813-819 ◽  
Author(s):  
Christiana Papoutsa ◽  
Diofantos G. Hadjimitsis
Keyword(s):  
Water Quality ◽  
Ground Truth ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Ground Truth Data ◽  
Suspended Particulate ◽  
Fibre Optic Probe ◽  
High Concentrations ◽  
Optic Probe ◽  
Remote Sensing Techniques

High concentrations of suspended particulate matter in reservoir waters directly affect the water treatment plants by occurring damages to the filters during the pretreatment. The objective of this project is to define the support that can be derived from the use of remote sensing techniques in order to determine the spatial variations of Total Suspended Solids (TSS) in Asprokremmos Dam in Paphos District in Cyprus. Such techniques have been successfully applied to the retrieval of TSS concentration and other water quality parameters in various geographical locations and environmental settings. This paper describes the results obtained by an existing running campaign in which in-situ spectroradiometric measurements and water sampling measurements of turbidity have been acquired at the study area. A GER-1500 field sperctro-radiometer equipped with a fibre optic probe is used to retrieve the spectral signatures of the Asprokremmos Dam and a portable turbidity meter is used for the determination of turbidity values. Ground-truth data based on the spectro-radiometric measurements were simulated to comply with the Landsat TM/ETM+ visible and infrared bands, so that identification of the ‘best-suited’ spectral region in which turbidity can be retrieved, was performed.

scholarly journals Effect of Maritime Traffic on Water Quality Parameters in Santa Marta, Colombia

2021 ◽  
Vol 9 (5) ◽  
pp. 474
Author(s):  
René Rodríguez-Grimón ◽  
Nestor Hernando Campos ◽  
Ítalo Braga Castro
Keyword(s):  
Water Quality ◽  
Petroleum Hydrocarbons ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Maritime Traffic ◽  
Natural Park ◽  
High Concentrations ◽  
The Impact ◽  
The Relationship ◽  
Multiple Chemical

Since 2013, there has been an increase (>23%) in naval traffic using maritime routes and ports on the coastal fringe of Santa Marta, Colombia. Of major concern, and described by several studies, is the relationship between maritime traffic and coastal contamination. This study proposed a maritime traffic indicator considering the simultaneous effects of several relevant measurements of water quality parameters to estimate the impact of naval activity. The approach involved developing a model including the number of vessels, hull length, and permanence time in berths. In addition, water quality variables, considering climatic seasons, were used to verify association with maritime traffic and touristic activities. The high concentrations of total coliforms (TC) and dissolved/dispersed petroleum hydrocarbons in chrysene equivalents (DDPH) reported by the International Marina of Santa Marta (SM) were affected by the local anthropic activities, including tourism, naval traffic, and urban wastewater discharges. Moreover, our results suggest the occurrence of multiple chemical impacts within Tayrona National Natural Park (PNNT) affecting conservation goals. The estimation of the maritime traffic indicator proposed in this study may be an easy and more complete tool for future studies evaluating the impact of naval activities on environmental quality.

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 Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

2020 ◽  
Vol 12 (10) ◽  
pp. 1586
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
Rodrigo Sepúlveda ◽  
Sergio I. Martinez-Martinez ◽  
Markus Disse
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Learning Approaches ◽  
Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

scholarly journals IoT for Monitoring and Control of Water Quality Parameters

2020 ◽  
Vol 14 (16) ◽  
pp. 4
Author(s):  
Fouzi Lezzar ◽  
Djamel Benmerzoug ◽  
Ilham Kitouni
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Monitoring And Control ◽  
Cloud Processing ◽  
Data Collection Process ◽  
Standard Values ◽  
Processing And Storage ◽  
And Control

<p class="0abstract"><span lang="EN-US">This work presents an Internet of Things (IoT) solution to facilitate real time water quality monitoring by enabling the management of collected data from electronic sensors. Firstly, we present in detail problems encountered during the used data collection process. We discuss after the requirements from the water monitoring quality standpoint, data acquisition, cloud processing and data visualization to the end user. We designed a solution to minimize technicians’ visits to isolated water tower, we designed sensors achieving a lifespan of several years. The solution will be capable of scaling the processing and storage resources. This combination of technologies can cope with different types of environments. The system also provides a notification to a remote user, when there is a non-conformity of water quality parameters with the pre-defined set of standard values.</span></p>

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 A COLLABORATIVE PLATFORM FOR WATER QUALITY MONITORING: SIMILE WEBGIS

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 201-207
Author(s):  
J. F. Toro Herrera ◽  
D. Carrion ◽  
M. A. Brovelli
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Stakeholder Participation ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Integrated Monitoring ◽  
Web Based ◽  
Border Regions ◽  
Subalpine Lakes ◽  
Collaborative Platform

Abstract. Nowadays, the increasing pressure over water resources is reflecting on the water quality all over the globe. Not surprisingly, local, and regional governments are taking initiatives into tackling this issue. However, the management of water resources requires coordinated management by the stakeholders, especially in cross-border regions, to achieve efficient regulations. Then, the data-sharing for monitoring the water resources is fundamental for the stakeholder participation in the process of knowledge building. This work presents the design and implementation of a collaborative web platform aiming at enhancing these processes applied to share water quality parameters maps produced under the framework of the SIMILE (Integrated monitoring system for knowledge, protection and valorisation of the subalpine lakes and their ecosystems) project. The platform takes advantage of open-source infrastructure and standards. The solution provides two web-based applications devoted to the upload/management (customized GeoNode) of the data and its visualization (WebGIS). The scope of the collaborative platform is to improve the access to information for awareness-building on the water resources in the Insubric area.

scholarly journals Mutual relationships of suspended sediment, turbidity and visual clarity in New Zealand rivers

2015 ◽  
Vol 367 ◽  
pp. 265-271 ◽  
Author(s):  
D. J. Ballantine ◽  
A. O. Hughes ◽  
R. J. Davies-Colley
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Aquatic Ecosystems ◽  
Water Quality Monitoring ◽  
Suspended Particulate ◽  
Sediment Mass ◽  
Data Scatter ◽  
Significant Interest ◽  
Sufficient Precision ◽  
Mass Concentrations

Abstract. Many river water quality monitoring programmes do not measure suspended particulate matter (SPM) mass concentrations despite significant interest in its multiple effects on aquatic ecosystems. Regular monthly sampling usually intercepts rivers in baseflow when suspended sediment mass concentrations and fluxes are relatively low and not of particular interest. New Zealand’s National Rivers Water Quality Network (NRWQN) is probably typical in not measuring SPM mass, although visual clarity and nephelometric turbidity are routinely measured. In order to better characterize SPM in NZ rivers, total suspended sediment (TSS) was temporarily added to the NRWQN. Turbidity, visual clarity and TSS are mutually inter-related over all 77 sites, although with considerable data scatter. However, within individual rivers turbidity and visual clarity are typically fairly closely related to TSS and provide fair to excellent surrogates. Therefore, TSS need not be measured routinely because it can be estimated with sufficient precision for many purposes from visibility or turbidity.

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