Development and Use of a Decision Support System for the Response to Drinking Water Contamination Events

2013 ◽  
pp. 205-221
Author(s):  
B.H. Tangena ◽  
J.F. Schijven ◽  
J.M. Foret ◽  
M. Bakker
Keyword(s):  
Drinking Water ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water Contamination ◽  
Drinking Water Contamination ◽  
Contamination Events

scholarly journals Water Expert: a conceptualized framework for development of a rule-based decision support system for distribution system decontamination

2014 ◽  
Vol 7 (1) ◽  
pp. 169-211
Author(s):  
J. L. Gutenson ◽  
A. N. S. Ernest ◽  
J. R. Fattic ◽  
L. E. Ormsbee ◽  
A. A. Oubeidillah ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Distribution System ◽  
Policy Science ◽  
Subject Matter Experts ◽  
Web Browser ◽  
Drinking Water Contamination ◽  
Parallel Data ◽  
Contamination Events

Abstract. Significant drinking water contamination events pose a serious threat to public and environmental health. Water utilities often must make timely, critical decisions without evaluating all facets of the incident, as the data needed to enact informed decisions are inevitably dispersant and disparate, originating from policy, science, and heuristic contributors. Water Expert is a functioning hybrid decision support system (DSS) and expert system framework, with emphases on meshing parallel data structures to expedite and optimize the decision pathway. Delivered as a thin-client application through the user's web browser, Water Expert's extensive knowledgebase is a product of inter-university collaboration that methodically pieced together system decontamination procedures through consultation with subject matter experts, literature review, and prototyping with stakeholders. This paper discusses development of Water Expert, analyzing the development process underlying the DSS and the system's existing architecture specifications.

scholarly journals IMPLEMENTASI METODE TOPSIS DAN SAW DALAM MEMBERIKAN REWARD PELANGGAN

2017 ◽  
Vol 4 (1) ◽  
pp. 88 ◽  
Author(s):  
Agus Perdana Windarto
Keyword(s):  
Drinking Water ◽  
Decision Support ◽  
Decision Support System ◽  
Computer Science ◽  
Support System ◽  
Research Method ◽  
Topsis Method ◽  
The Status ◽  
The Right ◽  
Better Than

<p align=""><em>In an industry sales, competition is a natural thing. The number of businesses with the same type makes an entrepreneur should have the right strategies in increasing the purchasing power of customers and reap the benefits. This research aims to implement the algorithms in computer science to create a decision support system for granting rewards to customers Drinking water Depot. In this research method used is TOPSIS and SAW. Where samples are used as much as 6 customers with the assessment criteria is the status of payments, the status of customer liveliness, long subscription, purchase amount, and the time of purchase. From the comparison of the two methods, showed that the calculations carried out by TOPSIS method is better than the SAW method.</em></p><p><strong><em>Keywords</em></strong><em>: Customer, SPK, Reward, TOPSIS method, Method SAW</em></p><p><em>Dalam sebuah industri penjualan, persaingan merupakan hal yang wajar. Banyaknya usaha-usaha dengan jenis yang sama membuat seorang pengusaha harus memiliki strategi-strategi yang tepat dalam meningkatkan daya beli pelanggan dan menuai keuntungan.</em><em> Penelitian ini bertujuan untuk mengimplementasikan algoritma dalam ilmu komputer untuk membuat sistem pendukung keputusan pemberian reward kepada pelanggan Depot Air minum. Dalam penelitian ini metode yang digunakan adalah TOPSIS dan SAW. Dimana sampel yang digunakan sebanyak 6 pelanggan dengan kriteria penilaian adalah </em><em>status pembayaran, status keaktifan pelanggan, lama berlangganan, jumlah pembelian, dan waktu pembelian</em><em>. Dari hasil perbandingan kedua metode tersebut, diperoleh hasil bahwa perhitungan yang dilakukan dengan metode TOPSIS lebih baik dibandingkan dengan metode SAW.</em></p><p><strong><em>Kata Kunci</em></strong><em>: </em><em>Pelanggan</em><em>, SPK, </em><em>Reward</em><em>,</em><em> </em><em>Metode </em><em>TOPSIS</em><em>, </em><em>Metode SAW</em><em></em></p>

Decision support system to select sustainable point-of-use/point-of-entry treatment systems (D4SPOUTS)

2013 ◽  
Vol 49 (2) ◽  
pp. 104-113
Author(s):  
Mohamed A. Hamouda ◽  
William B. Anderson ◽  
Peter M. Huck
Keyword(s):  
Drinking Water ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Distribution System ◽  
Sustainability Assessment ◽  
Engineering Practice ◽  
Point Of Entry ◽  
Point Of Use ◽  
Treated Drinking Water

Point-of-use (POU) and point-of-entry (POE) devices are, in some situations, considered to be a viable solution for drinking water suppliers and consumers alike to deal with site specific drinking water issues. This paper introduces a newly developed decision support system (DSS) that employs decision making techniques to select among the various devices based on their characterization and sustainability assessment. Careful illustration of the various aspects and components of the DSS is provided and the decision process is explained. Aspects of validity, usability and sensitivity analysis are demonstrated through a hypothetical case study for removing lead introduced in the distribution system of municipally treated drinking water. The output of the DSS helps to determine the more sustainable treatment devices which should have positive implications for the application of POU and POE devices. Other potential uses of the DSS are described to illustrate its versatility and usefulness. The DSS is not intended to replace common engineering practice in selecting POU and POE treatment systems, but rather to give support to the users by providing the necessary information about all possible solutions.

scholarly journals Probabilistic Backtracing of Drinking Water Contamination Events in a Stochastic World

2014 ◽  
Vol 70 ◽  
pp. 1688-1696 ◽  
Author(s):  
P. van Thienen ◽  
D. Vries ◽  
B. de Graaf ◽  
M. van de Roer ◽  
P. Schaap ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Contamination ◽  
Drinking Water Contamination ◽  
Contamination Events

scholarly journals Implementation of an environmental decision support system for controlling the pre-oxidation step at a full-scale drinking water treatment plant

2020 ◽  
Vol 81 (8) ◽  
pp. 1778-1785 ◽  
Author(s):  
Lluís Godo-Pla ◽  
Pere Emiliano ◽  
Santiago González ◽  
Manel Poch ◽  
Fernando Valero ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Predictive Model ◽  
Support System ◽  
Drinking Water Treatment ◽  
Oxidation Step ◽  
Environmental Decision

Abstract Drinking water treatment plants (DWTPs) face changes in raw water quality, and treatment needs to be adjusted to produce the best water quality at the minimum environmental cost. An environmental decision support system (EDSS) was developed for aiding DWTP operators in choosing the adequate permanganate dosing rate in the pre-oxidation step. To this end, multiple linear regression (MLR) and multi-layer perceptron (MLP) models are compared for choosing the best predictive model. Besides, a case-based reasoning (CBR) model was approached to provide the user with a distribution of solutions given similar operating conditions in the past. The predictive model consisted of an MLP and has been validated against historical data with sufficient good accuracy for the utility needs (R2 = 0.76 and RSE = 0.13 mg·L−1). The integration of the predictive and the CBR models in an EDSS gives the user an augmented decision-making capacity of the process and has great potential for both assisting experienced users and for training new personnel in deciding the operational set-point of the process.

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