Drinking Water Source Monitoring Using Early Warning Systems Based on Data Mining Techniques

2018 ◽  
Vol 33 (1) ◽  
pp. 129-140 ◽  
Author(s):  
Ianis Delpla ◽  
Mihai Florea ◽  
Manuel J. Rodriguez
Keyword(s):  
Data Mining ◽  
Drinking Water ◽  
Early Warning ◽  
Source Monitoring ◽  
Water Source ◽  
Early Warning Systems ◽  
Drinking Water Source ◽  
Warning Systems ◽  
Data Mining Techniques

A Residual Chlorine Removal Method to Allow Drinking Water Monitoring by Biological Early Warning Systems

2005 ◽  
Author(s):  
Willian H. VAN DER Schalie ◽  
David E. Trader ◽  
Mark W. Widder ◽  
Tommy R. Shedd ◽  
Linda M. Brennan
Keyword(s):  
Drinking Water ◽  
Early Warning ◽  
Early Warning Systems ◽  
Water Monitoring ◽  
Residual Chlorine ◽  
Warning Systems ◽  
Removal Method ◽  
Chlorine Removal

scholarly journals Centinela: an early warning system for the water quality of the Cauca River

2014 ◽  
Vol 16 (6) ◽  
pp. 1409-1424 ◽  
Author(s):  
Carlos Vélez ◽  
Leonardo Alfonso ◽  
Arlex Sánchez ◽  
Alberto Galvis ◽  
Gilberto Sepúlveda
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Early Warning ◽  
Early Warning System ◽  
Treatment Plant ◽  
Warning System ◽  
Water Source ◽  
Early Warning Systems ◽  
Drinking Water Source ◽  
The City

The Cauca River is the drinking water source for 1.3 million inhabitants of the city of Cali, Colombia. Although the river discharge is sufficient to handle the water demand of the city all year long, significant water pollution events cause frequent disruption to the Puerto Mallarino Treatment Plant (PMTP) and the water supply service, with substantial social and economic impacts on the city. The sources of pollution include wastewater discharges upstream of the PMTP and important sediment transport from the upstream sub-catchments during heavy rainfall events. Both situations can lead to a closure of the PMTP when the presence of a pollution plume at its intake is evident. This paper presents the design and prototype of a water quality early warning system to anticipate the peaks of pollution in the river, in order to assist the operators in taking timely informed decisions about the operation of the treatment plant. As the published experiences of early warning systems for similar water pollution problems are very limited, the approach to solve the problem using hydroinformatics technologies is worth documenting for utility companies with a similar problem.

Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries

Fuzzy Systems ◽  
2017 ◽  
pp. 202-234
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Fuzzy Expert System ◽  
Warning Systems ◽  
Single Data ◽  
Set Up

Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.

Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries

2015 ◽  
pp. 763-796 ◽  
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mrsic
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Fuzzy Expert System ◽  
Warning Systems ◽  
Single Data ◽  
Set Up

Early warning systems are made with purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible and with significant relevance. There are numerous ways to set up early warning systems within company. Those solutions are often based on single data mining methods, and they rarely provide the holistic and qualitative approach needed in modern market uncertainty conditions. This chapter gives a novel concept for early warning system design within company, applicable in different industries. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas in addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. As part of this framework, SNA methods are also explained and introduced as a very powerful and unique tool to be used in modern early warning systems.

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