Columbia Southern University Prioritizing Projects Research Paper Conduct research using the Online Library and find at least two articles on prioritizing

Columbia Southern University Prioritizing Projects Research Paper Conduct research using the Online Library and find at least two articles on prioritizing projects. Compare and contrast the two articles you found and create a three- to four-page Word document (synopsis) in APA format on what you found different about each article.Other items that need to be addressed in your paper include the urgency of each project, the cost of delaying the expected benefits from various projects, and practical details concerning the timing.Include a title page and a reference page (not included in the total number count of pages). Copyright of Journal of Construction Engineering & Management is the property of American Society of Civil
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URBAN WATER JOURNAL
2018, VOL. 15, NO. 6, 561–567
https://doi.org/10.1080/1573062X.2018.1529806
RESEARCH ARTICLE
Expert system for selecting and prioritizing projects for handling urban water
supply crises
Welitom Ttatom Pereira da Silvaa and Marco Antonio Almeida de Souzab
a
Department of Sanitary and Environmental Engineering, Federal University of Mato Grosso, Cuiabá, Brazil; bDepartment of Civil and
Environmental Engineering, University of Brasília, Brasília, Brazil
ABSTRACT
ARTICLE HISTORY
The water supply crisis (UWC) has affected various cities around the world. The variability of possible
causes, the many viable alternatives to UWC management and methodologies for selecting these
alternatives, as well as local government’s economic and technical constraints make the problem
complex. The aim of this paper is to help select a set of alternative solutions suitable for the UWC
problem. The proposed methodology comprised the following steps: (1) theoretical foundation, (2)
planning the expert system (ES) to be built, (3) formal knowledge explicitation, (4) knowledge coding,
(5) evaluation and adequacy of ES and (6) application of ES to real-life UWC cases. The main result was a
computational decision support system, called UWC-ES. The conclusion was that UWC-ES behaved as a
computational tool that reasonably reproduces knowledge from various human experts with acceptable applicability, and considering the possibility of using it in other cases.
Received 24 January 2018
Accepted 24 September 2018
1. Introduction
The urban water supply crisis (UWC) is currently a significant
problem affecting many populations around the world.
Numerous UWC cases can be found in the literature, such
as the city of São Paulo (Brazil), the provinces of northern
and western China, California (USA), the city of Cape Town
(South Africa), and the western prairie provinces of Canada,
which have been described in Coutinho, Kraenkel, and Prado
(2015); Zheng et al. (2010); Pollak (2010); Ziervogel, Shale,
and Du (2010), and Schindler and Donahue (2006), respectively. This specific problem has motivated researchers to
seek alternative solutions and methodologies to cope with
them adequately.
The alternative solutions are varied and may consider structural strategies (technological options to reduce water consumption, such as using water-saving equipment), nonstructural (actions that influence demand, such as changes to
pricing policies) and the combination of structural and nonstructural strategies. A more detailed discussion of structural
and non-structural strategies is presented in Savenije and Van
der Zaag (2002). The analysis methodologies for handling the
UWC include traditional optimization methods, simulation and
scenario generation techniques, statistical models, multiobjective and multicriteria methods, among others. For example,
Zarghami, Abrishamchi, and Ardakanian (2008) carried out
studies aiming to select alternative water management measures in an environment with significant population growth
and frequent water supply failures (in the case of the city of
Zahedan, Iran). A multiobjective and multicriteria model for
the problem of water supply contemplating several variables
(losses in the water network, consumption measures and
others) was developed. Different criteria (costs, need for
CONTACT Welitom Ttatom Pereira da Silva
wttatom@gmail.com
© 2018 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
Management strategies;
rule-based expert system;
water crisis
water supply, etc.) were aggregated using the Compromise
Programming method. The results showed that demand management measures can delay water transfer projects to the
city of Zahedan for more than 10 years. Artificial intelligence
techniques have also been used (León et al. 2000; Tillman
et al. 2005; López-Paredes, Saurí, and Galán 2005).
To analyze this context where there are various alternative solutions and different methodologies, faced with situations of severe limitations of financial and human resources
that many Brazilian cities and cities throughout the world
commonly go through, the following question arises: how
to select alternative solutions for a given UWC problem? As
a response, using UWC classification techniques and constructing an expert system is suggested based on studies by
Silva and Souza (2017) and Liao (2005). Thus, this study aims
to help select suitable project alternatives for the UWC
problem. More specifically, it is hoped that a system can
be obtained to support the decision-making process of
selecting priority projects to solve the UWC problem in
urban environments with a significant limitation of financial
and human resources.
2. Methods
The proposed methodology comprised the following steps: (1)
understanding the real problem using a literature review and
a study of the theoretical foundations of possible solutions, (2)
planning the expert system to be developed, (3) formal knowledge explicitation, (4) knowledge coding and development of
the expert system (ES), (5) evaluation and adequacy of the ES
and (6) application of the ES to real-life UWC cases to verify
the acceptability of the developed ES response.
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W. T. P. D. SILVA AND M. A. A. D. SOUZA
2.1 Theoretical foundation
In this section, topics such as UWC classification (typification)
and the expert system (ES) are presented, which are the basis
for this study.
Using typical cases (case classification) plays an important
role in decision-making, especially when the decision involves
a large number of indicators and/or influencing factors (LópezParedes, Saurí, and Galán 2005; UNEP/UNESCO 1987). In this
study, the decision support model for crisis management in
urban water supply (UWC-MODEL), developed by Silva and
Souza (2017), was used to simplify the analysis and study
different UWC situations (classification of UWC cases).
The UWC-MODEL performs the following activities: (1) it
aggregates influential factors in the UWC into five levels
(socioeconomic, management, environmental, urban and cultural), (2) it evaluates its intensity of contribution to the UWC
situation for each level and (3) based on this evaluation, it
classifies the UWC situation at each level (classes: very strong,
strong, moderate, weak and very weak). The UWC-MODEL can
classify and/or typify UWC cases and, consequently, the cause
of the UWC is identified, helping to select and prioritize projects handling UWC. For example, in a case that has a very
strong contribution from the cultural level, the measures to
restructure the urban water supply system should prioritize
projects related to the cultural level. Therefore, setting up an
environmental education program could be an appropriate
project for handling UWC. In Equations (1) and (2), results
from the UWC-MODEL (RUWC-MODEL), the basis and starting
point of this study, are presented in vector format, where
Cj=1, Cj=2, Cj=3, Cj=4 and Cj=5 are the classes of socioeconomic,
management, environmental, urban and cultural levels,
respectively. More details about the UWC-MODEL can be
found in the study by Silva and Souza (2017).

RUWCMODEL ¼ Cj¼1 ; Cj¼2 ; Cj¼3 ; Cj¼4 ; Cj¼5
(1)
RUWCMODEL ¼ ffo; mfr; fr; mo; mfog
(2)
Another basis for this research was using the technique to
generate expert systems (ES). Artero (2009) defined ES as a
computational system designed to represent the knowledge
of one or more human experts on a particular domain and,
from the processing of the knowledge base, seek solutions to
problems that, in general, require a great deal of specialized
knowledge.
In an ES operation, it is assumed that the user feeds the ES
with factors or information and the system provides the user
with expert knowledge. Internally, ES consists of two main
components: the knowledge base and inference engineering.
The knowledge base stores knowledge and inference engineering uses stored knowledge to construct the conclusions.
Some basic concepts refer to the problem domain, the domain
knowledge and the inference engineering. A problem domain
refers to a problem specific to an area (medicine, finance,
science or engineering) that the expert can solve. The expert’s
knowledge of how to solve a specific problem is called domain
knowledge. Inference engineering refers to the ability the ES
has to infer in the same way a human expert should infer
when faced with a problem.
The general strategies for ES development are shown by
Giarratano and Riley (2004). Briefly, the ES development process consists of: (1) the ES developer establishes a dialogue
with the experts for the expert knowledge explicitation, (2) the
developer encodes the explicit knowledge (ES development),
(3) the experts evaluate and criticize the developed ES, the
developer makes adjustments and the process is repeated
until the ES is considered adequate by the experts. In practice,
the ES is an executable program that searches for the knowledge about its domain in a separate file. This means that the
knowledge base can be completely changed and even then,
the program will work normally, adopting the knowledge from
the new base (Artero 2009). Some suggested references on
the subject are: Kim, Wiggins, and Wright (1990); Wright et al.
(1993); Nikolopoulos (1997); Resende et al. (2005); Artero
(2009); Giarratano and Riley (2004) and Liao (2005).
2.2 Expert system planning
The purpose of the ES planning stage was to produce a formal
plan for ES development called the UWC-ES. Thus, the feasibility assessment, resource management and preliminary functional
layout
tasks
were
performed
based
on
recommendations made by Giarratano and Riley (2004). For
the feasibility assessment task, the factors and returns suggested by Giarratano and Riley (2004) were verified, in order to
decide if the ES approach would be adequate. The resource
management task was carried out by researching the computer resources (software and hardware), human resources and
financial resources to develop the UWC-ES. In order to do this,
a literature review of the resources used to develop precursor
ESs with similar objectives was carried out, and a comparison
was made with the resources available to develop the UWCES. The preliminary functional layout task should define what
the system will achieve by specifying the system functions.
Thus, the objectives of the ES were carefully analyzed in order
to define the functions of the system, following recommendations by Giarratano and Riley (2004).
2.3 Formal knowledge explicitation
Knowledge explicitation refers to the process of acquiring the
knowledge needed to solve the problem (domain knowledge).
To do this, the activities used by Collier, Leech, and Clark
(1999); Tillman et al. (2005) and Patlitzianas, Pappa, and
Psarras (2008) were adapted. In this case, these activities
included: (1) defining the population universe of simulated
UWC cases, (2) defining the sample analyzed by the experts,
(3) identifying projects for handling UWC and (4) obtaining
domain knowledge. A total of 13 specialists (five with a master’s degree and seven with a doctorate degree) were considered, of which six were working in the sanitation area, two in
the environment area and five in the water resources area, six
linked to water regulatory agencies, two to the environmental
protection agency and five to research institutions and
universities.
The population universe of simulated UWC cases is the
total possible number of combinations of the UWC-MODEL
classifications. Thus, 3125 (five levels and five classifications,
URBAN WATER JOURNAL
N = 55) individuals or typologies of simulated UWC cases were
observed that form the population universe. To define the
sample to be analyzed by the experts, the simple random
sample method was used. As justification, this method of
sampling leads to the sample in which each typology of the
sample population has the same probability of being selected,
not privileging specific situations or cases. The number of
sample units (n) was defined in 10% of the population,
which made a total of 313 typologies analyzed by the experts.
To identify the projects for handling UWC, a literature review
was carried out. Identifying priority projects (PP) for handling
UWC by experts for the ‘n’ sample units yielded the training
database, an initial part of the task of obtaining the domain
knowledge. For this purpose, the UWC (UWC-MODEL) classification and/or typology information, the identification of projects for handling UWC, the sampling technique used and the
samples to be analyzed were made available to the experts.
The experts were then asked to identify PP for handling UWC
(selection of five major projects for handling UWC) for each of
the typologies of the real-world/simulated cases analyzed by
them. For exemplification, from the process of obtaining the
training database, a graphical representation is illustrated in
Figure 1.
Having defined the training database, the final part of obtaining the knowledge domain (obtaining the rules) was started.
Moreover, a machine learning technique was used for this purpose, which automatically extracts information from the training
database. More specifically, a decision tree was used as the
classification model, which is one of the most widely used
machine supervised learning methods in practice (Artero 2009).
The method is based on the decision tree construction, from the
training database to obtaining the production rules (domain
knowledge). For the construction of the decision tree, algorithm
J48, which is one of the most known and used algorithms for
constructing decision trees, was used (Artero 2009). To evaluate
the classification model (decision tree), the Confusion Matrix and
Kappa Statistics (κ) were used, as recommended by Resende
et al. (2005). Furthermore, it was considered that the classification model would be adequate if it presented Kappa Statistics (κ)
values equal or above κ = 0.41 (moderate agreement), according
to Landis and Koch (1977). Otherwise, adjustments in the classification model would be necessary.
2.4 Knowledge coding
For knowledge coding, a Pentium 2.13GHz microcomputer
was used, with 4GB of RAM in the Windows operating system
Figure 1. Obtaining the knowledge domain.
563
using CLIPS (C Language Integrated Production System) shell,
version 6.3. In this case, it was adopted as a robust and
efficient shell for ES development, one that: (1) presented
the ability to resolve conflicts between rules, (2) operated
satisfactorily with the forward chain, (3) was a free access
shell and (4) presented good answers (accuracy). This robust
shell definition considered the existence of conflicting opinions among the experts consulted, the proposition of the
Modus Ponens type ‘if (condition) – then (action)’ as an appropriate form of inference, and the economic limitation for
commercial shell acquisition. Thus, the CLIPS shell can be
evaluated as robust to the problem in focus agreeing with
the works of Riley et al. (1987); Mettrey (1991), and Kuesten
and McLellan (1994).
2.5 Evaluation and adequacy
According to Giarratano and Riley (2004), at this stage, the
expert should evaluate and criticize the UWC-ES, passing on
this information to the ES developer, who in turn performs the
adjustments and again returns the ES to the expert for reevaluation. This process is iterative until the expert judges that
UWC-ES is adequate. Considering the characteristics of the
problem and the studies carried out by Spring (1997) and
Collier, Leech, and Clark (1999), the Turing test (a classic test
that aims to verify if a machine has the intelligence matching
that of a human). To implement the test, the methodologies
used by Spring (1997); Collier, Leech, and Clark (1999), and
Artero (2009) were adjusted.
The Turing test is based on forming three groups of different experts, indicated here by G-1, G-2 and G-3. The test
basically consists of collecting a set of ‘m’ test cases, previously
solved by experts from the G-1 group, solving these cases by
developed ES (G-2), carrying out the specific evaluation of
both solutions, S (G-1) and S (G-2) by other experts (G-3). In
the specific evaluation, two outputs were requested from the
G-3 group; the first output refers to the quality evaluation of
the G-1 and G-2 solutions, according to a scale ranging from 1
to 7 (1 = very bad, 4 = reasonable, 7 = very good). In the
second output, the identification of the solutions from the ES
was requested. If G-3 assigns a value greater than or equal to 4
to the quality of solutions presented by G-2 and cannot determine (with a minimum of 50% accuracy) which one of the two
(G-1 or G-2) is the group of experts, it is said that the machine
has passed the Turing test and therefore can simulate human
intelligence. In this case, the end of the UWC-ES development
is observed, and the ES is considered suitable to select the
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W. T. P. D. SILVA AND M. A. A. D. SOUZA
best solutions for the UWC problem. Otherwise, adjustments
must be made in the UWC-ES.
2.6 Application of the expert system
The purpose of the application cases was to help evaluate
the results of the developed ES model. Considering the
prospect of possible water supply problems in the Federal
District, as mentioned by Conejo et al. (2009), some of the
Administrative Regions (AR) of the Federal District were
adopted as case studies. These AR included Brasília, Lago
Norte, Cruzeiro, Guará, Varjão, Estrutural and Park Way.
These AR were chosen according to the importance of
studying urban environments with different economic levels.
For ES application, secondary information was used, based
on data from Silva (2012). In addition, the Federal District
Government was considered as the decision-maker in the
case, with its respective competent institutions (Brazilian
Federal District’s Regulatory Agency for Water, Energy and
Sanitation – ADASA, Brazilian Federal District’s Water
Supplier and Sanitation Company – CAESB, Brazilian
Institute of Environment and Water Resources – IBRAM and
Secretary of State for the Environment – SEMA).
3. Results
Based on following the formal plan and setting the predefined
tasks for UWC-ES development (expert system planning stage),
responses about its viability were obtained. The result of the
feasibility assessment task, the verification of the factors and
returns suggested by Giarratano and Riley (2004), led to the
return of the viability response of the ES approach. The reasons that led to this response refer to the fact that most of the
returns (factors 1, 3, 4, 5 and 6) showed a favorable return to
ES development, as shown in Table 1.
For the resource management task, the result indicated
that the available resources are comparable to the resources
used to develop other ES with equivalent functions, according
to the literature review (Cheng, Yang, and Chan 2003; Chau,
Chuntian, and Li 2002; León et al. 2000). Based on the preliminary functional layout task, it was found that the proposed
ES must ensure compliance of the purpose of pointing out
priority projects for handling UWC. From the knowledge explicitness stage, the population universe (possible combinations,
which make a total of N = 3125) and the identification (Id.) of
the sample units (typologies, totalizing n = 313) were identified to be studied.
Table 1. Factors and returns considered in the ES viability assessment.
Item
Factora
Returnb Evaluationc
1
Can the problem be solved efficiently by
No
No
conventional programming…
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