WaterScope: an innovative water quality information management system

2004 ◽  
Vol 4 (5-6) ◽  
pp. 409-414
Author(s):  
J.R. Howard ◽  
J. Lucas ◽  
J. Maitland ◽  
P. Tarrant ◽  
T. Watson
Keyword(s):  
Water Quality ◽  
Management System ◽  
Information Management System ◽  
Geographical Information ◽  
Direct Access ◽  
Quality Data ◽  
Incident Management ◽  
Quality Information ◽  
Management Support ◽  
Water Quality Data

SA Water is a State owned organisation that owns and manages South Australia's water supplies, providing reliable drinking water to nearly 1.4 million South Australians. A major issue affecting SA Water's ability to manage water quality effectively has been the difficulty accessing water quality information which has been stored in separate, generally inaccessible databases with poor reporting and decision support capability. To improve SA Water's ability to make timely and effective decisions regarding water quality, an integrated business system has been developed which provides water managers with direct access to comprehensive water quality information. The system includes improved field data collection units which incorporate a barcode system; sample point images and workflow support tools; an integrated water quality data warehouse with automated standard and ad hoc reporting capabilities; a geographical information system containing comprehensive coverages of natural resources and system infrastructure information; and water incident exception reporting and incident management support through a corporate incident management system. Major benefits of the system will include improved management of public health risk through quicker and more accurate reporting of incidents; improved customer confidence in SA Water; improved knowledge capture and visibility of water quality information; increased efficiency of capital utilisation and better understanding of system performance through spatial representation of data and trending of results. WaterScope can also be used and shared by data partners and regulators, making optimal use of the State's limited water quality data sets. It can also be made available commercially to other water management organisations. Future challenges include the integration of wastewater and recycled water data, linking of continuous (on-line) water quality data and links to water demand management systems.

Implementation of a large scale water quality data management system

1976 ◽  
Vol 10 (1) ◽  
pp. 31-36 ◽  
Author(s):  
William D. Haseman ◽  
Clyde Holsapple ◽  
Andrew B. Whinston
Keyword(s):  
Water Quality ◽  
Data Management ◽  
Management System ◽  
Large Scale ◽  
Data Management System ◽  
Quality Data ◽  
Water Quality Data

scholarly journals GIS-based methodologies for assessing nitrate, nitrite and ammonium distributions across a major UK basin, the Humber

2004 ◽  
Vol 8 (4) ◽  
pp. 823-833 ◽  
Author(s):  
H. Davies ◽  
C. Neal
Keyword(s):  
Water Quality ◽  
River Network ◽  
Geographical Information ◽  
Quality Data ◽  
Monitoring Site ◽  
River Channels ◽  
Mean Flow ◽  
Water Quality Data ◽  
Catchment Characteristics ◽  
Humber River

Abstract. The distributions of nitrate, nitrite and ammonium at various monitoring sites across the Humber basin (area 24 000 km2) were examined within a Geographical Information System (GIS) framework. This basin contains diverse characteristics, from areas of high population and industry to rural and arable regions. The Humber River is a major provider of and nutrient fluxes to the North Sea from the UK. Within the GIS analysis, the distributions of mean and mean flow weighted concentrations, flux and flux per unit area, were investigated. Empirical relationships between land characteristics and water quality for the whole catchment draining to each water quality monitoring site were established. Thirty-eight catchments were chosen for this analysis, with areas ranging from 46 km2 to 8225 km2. These catchments are distributed across the Humber, encompassing the different conditions across the basin, thus allowing relationships between water quality and catchment characteristics to be used to estimate the nitrogen concentrations and flux throughout the basin river network. The main water quality data source was the Land Ocean Interaction Study (LOIS) dataset. The Environment Agency of England and Wales water quality datasets were used to infill areas of sparse LOIS monitoring network density within the Humber. The work shows the feasibility of estimating nitrate and, to a lesser extent, nitrite and ammonium concentrations and fluxes across the river network based on land characteristics, using a GIS methodology. The estimations work particularly well for the main river channels. However, there are local anomalies which are more difficult to predict. Maps showing concentration variations at 500 m intervals along the Humber basin river networks are presented; these are of particular value for environmental managers and socio-economists. Keywords: GIS, nitrate, nitrite, ammonium, catchment characteristics

scholarly journals A new online monitoring and management system for accidental pollution events developed for the regional water basin in Ningbo, China

2011 ◽  
Vol 64 (9) ◽  
pp. 1828-1834 ◽  
Author(s):  
Gaosheng Zhang ◽  
Linlin Chen ◽  
Yuedan Liu ◽  
TaeSoo Chon ◽  
Zongming Ren ◽  
...  
Keyword(s):  
Water Quality ◽  
Management System ◽  
Online Monitoring ◽  
Water Quality Management ◽  
Warning System ◽  
Quality Data ◽  
Self Organizing Map ◽  
Water Quality Data ◽  
Accidental Pollution ◽  
Pollution Events

Due to urgency of the accidental pollution events (APE) on one side and the variability in water quality data on the other side, a new online monitoring and management system (OMMS) was developed for the purpose of sustainable water quality management and human health protection as well. The Biological Early Warning System (BEWS) based on the behavioral responses (behavior strength) of medaka (Oryzias latipes) were built in combination with the physico-chemical factor monitoring system (PFMS) in OMMS. OMMS included a monitoring center and six monitoring stations. Communication between the center and the peripheral stations was conducted by the General Packet Radio Service (GPRS) network transmission complemented by a dial-up connection for use when GPRS was unavailable. OMMS could monitor water quality continuously for at least 30 days. Once APEs occurred, OMMS would promptly notify the administrator to make some follow up decisions based on the Emergency Treatment of APE. Meanwhile, complex behavioral data were analyzed by Self-Organizing Map to properly classify behavior response data before and after contamination. By utilizing BEWS, PFMS and the modern data transmission in combination, OMMS was efficient in monitoring the water quality more realistically.

Machine learning-based tools for water digitalisation

2021 ◽  
Author(s):  
Asma Slaimi ◽  
Susan Hegarty ◽  
Fiona Regan ◽  
Michael Scriney ◽  
Noel O’Connor
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Data Warehouse ◽  
New Technologies ◽  
Short Term Memory ◽  
Water Quality Management ◽  
Water Levels ◽  
Quality Data ◽  
Quality Information ◽  
Water Quality Data

<p>Advanced technologies have proven to deliver significant outcomes in the water management sector. New technologies provide the capability to collect and correlate the information from remote devices, introducing smart tools that can leverage augmented intelligence for interpreting structured and unstructured, text-based or sensory data. However, most of the single feature or non-sequential prediction machine learning methods for understanding water quality achieve poor results due to the fact that water quality information exists in the form of multivariate time-series datasets.</p><p>At the catchment scale, there are many layers where relevant data needs to be measured and captured. For that, data warehouses play an essential role in decision support systems as they provide adequate information. </p><p>In this paper, we started by extracting, transforming, cleaning and consolidating data from several data sources into a data warehouse. Then, the data in the warehouse was used to develop a computer tool to predict river water level using Artificial Neural Networks (ANNs), in particular, Long Short-Term Memory networks (LSTM). As the prediction performance is significantly affected by the model inputs, the feature selection step, which considers the multivariate correlation of water quality information in terms of similarity and proximity, is particularly important. The features obtained from the previous steps are the inputs to the prediction model based on LSTM, which naturally takes the time sequence of water quality information into account.</p><p>The proposed method is applied to two different catchments in the island of Ireland. Experimental results indicate that our model provides accurate predictions for water levels and is a useful supportive tool for water quality management. </p><p>Ultimately, digitised representations of water environments will guarantee situational awareness of water flow and quality monitoring. The digitalisation of water is no longer optional but a necessity to solve many of the challenges faced by the water industry.</p><p><br><strong>Keywords:</strong> Water digitalisation, water quality, data warehouse, machine learning, predictive model, LSTM.</p><p><br><br></p>

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