JURNAL 1 :
1. TEMA :
Ground Water Management
2. PENGARANG, TAHUN, JUDUL.
- Pengarang :
Chen Wuing Liu
(Professor, Department of Bioenvironmental Systems Engineering,
National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan
10617, R.O.C.)
E-Mail: lcw@gwater.agec.ntu.edu.tw.
- Tahun :
April, 2004
- Judul :
DECISION SUPPORT SYSTEM FOR MANAGING GROUND WATER RESOURCES IN
THE CHOUSHUI RIVER ALLUVIAL IN TAIWAN
(JOURNAL OF THE AMERICAN WATER RESOURCES
ASSOCIATION)
3. MOTIVASI / LATAR BELAKANG.
Ground water is a vital water resource in the Choushui River
alluvial fan in Taiwan. A significantly increased demand for water, resulting
from rapid economic development, has led to large scale ground water
extraction. Overdraft of ground water has considerably lowered the ground water
level, and caused seawater intrusion, land subsidence, and other environmental
damage.
4. MASALAH & TUJUAN.
- Masalah:
Ground water overdraft has caused serious environmental problems,
including seawater intrusion, ground water salinization and land subsidence
- Tujuan :
This study presents a decision support system (DSS) for managing
ground water resources in the Choushui River alluvial fan
5. METODOLOGI.
- Data & Sumber Data:
This study classifies wet, average and dry years based on annual
precipitation data from 1990 to 1999 (Central Weather Bureau, 1990-1999).
- Variable :
Permissible yield, ground water level and drawdown, land
subsidence, seawater intrusion, and ground water quality.
- Model Penelitian :
This DSS integrates geographic information, ground water
simulation, and expert systems. The geographic information system effectively
analyzes and displays the spatially varied data and interfaces with the ground
water simulation system to compute the dynamic behavior of ground water flow
and solute transport in the aquifer. Meanwhile, a ground water model,
MODFLOW-96, is used to determine the permissible yield in the Choushui River
alluvial fan. Additionally, an expert system of DSS employs the determined
aquifer permissible yield to assist local government agencies in issuing water
rights permits and managing ground water resources in the Choushui River
alluvial fan.
6. HASIL & REKOMENDASI.
- Hasil :
Comprehensive hydrogeological data are gathered to provide the
user with access to the most up-to-date information on the Choushui River
alluvial fan. The simulation module uses these data to determine the
hydrogeological response to various scenarios. The simulated results are
inputted to the expert system to perform an expert diagnosis for assessing the
impact of developing various ground water resources on conservation and offers
expert advice to help decision makers. The DSS has been applied to determine
the permissible yield of aquifers and issue water rights permits. The
MODFLOW-96 model performs a simulation to obtain the relationship of pumping rate
to drawdown for wet, average, and dry hydrological years. The Hill method,
which plots simulated pumping rate against drawdown, is employed to determine
the permissible yield. An expert system of the DSS then applies the determined
aquifer permissible yield to assist local government agencies in issuing water
rights permits. The DSS described here provides a useful tool for assisting
decision makers in managing ground water resources.
- Rekomendasi :
-
JURNAL 2 :
1. TEMA :
Decision
Support System For The Real Estate Industry
2. PENGARANG, TAHUN, JUDUL.
- Pengarang :
Raul
Valverde
(John Molson School of Business, Concordia
University, 1455 de Maisonneuve Blvd W., Montreal Canada)
- Tahun :
2011
- Judul :
A RISK MANAGEMENT DECISION SUPPORT SYSTEM
FOR THE REAL ESTATE INDUSTRY
(International Journal of
Information and Communication Technology Research)
3. MOTIVASI / LATAR BELAKANG.
In the last two decades the risk management
finance theory has been applied to the real estate decision making process but
with not much effort of the business or academia to develop a risk management
decision support system that integrates this knowledge, and transmits the
theory to real-world practice.
Property managers face every day critical
risk management decisions as determining the price for sell or rent of a
property, choice of financing, investment analysis, real estate portfolio
management, real estate valuation. In these cases a decision support system can
be very valuable in order to minimize the risk of potential losses due to wrong
decisions.
4. MASALAH & TUJUAN.
- Masalah:
The main risk management related decisions
that real estate professionals made on a daily basis
- Tujuan :
The
objective of our system is to support risk management decisions in the real
estate industry. The primary users of the system are investors; they will use
the system to support their investment decisions as the kind of property to
invest in order to maximize their return. They will use the system to monitor
the performance of the corporate building and to support how to finance the
property and the amount of prepayment.
5. METODOLOGI.
- Data & Sumber Data:
The
database of the decision support is populated with a sample of data collected
from the real estate market of the state of California USA.
- Variable :
Real Estate
Industry
- Model Penelitian :
(Current Property
Value) = (Present Value of Net Rents Over Next T Years + Present Value of
Expected Market Price in T years)
6. HASIL & REKOMENDASI.
- Hasil :
The reports that our DDS will generate are:
Financing choice report: It will suggest the best financing choice (ARM or
AFM), and the probability associated with it.
Performance Report: It will show how the corporate building is used in terms of
profitability and will suggest actions to be taken in order to improve
profitability of the corporation related to the building.
Investment report: It will suggest the assets to be bought in order to achieve
a minimum risk, and the expected level of return of investment for this choice.
Investment property valuation: It will show the investment potential value of a
property.
Amount of prepayment for a mortgage: It will suggest
the percentage of prepayment for the mortgage.
A
prototype of the DDSS was implemented in Visual Basic .Net. The system’s main
menu is shown in figure 5. The main menu is organized in four major functional
areas: Investment decision support utilities, financing decision support tools
and performance evaluation support tools.
The financing module suggests what kind of
financing (ARM or FRM) shall the user chose in order to get a better rate. The
module also gives the probability of the accuracy of the decision. The input
variables are the region CPI, the ARM rate, the FM rate and one the year
Treasury bill rate. In the example of figure 23 the CPI is 11%, the ARM rate is
6%, the FRM rate is %8 and the treasury bill rate is %4, for this example the
module suggests the ARM mortgage with a success probability of 0.96.
The performance decision support tools are
composed of the investment valuation and corporate performance study modules.
The first evaluates a property in terms of investment, given a property value,
yearly rent, yearly operating costs, property number of years giving cash flow,
resale value after the given number of years and the desired rate of return.
The module evaluates the investment and reports the net present value of the property,
which can be used for decision purposes. Figure 10 shows an example of this
module, where the property value is $105,000, yearly rent $680, yearly expenses
of $3000, number of years of 25, resale value of $40,000 and desired rate of
return of %10, giving a result of net present value of -$122,561. In this case
the system suggests that the investor should not buy the property.
The corporate performance study module
calculates the difference of the net present value of the firm with and without
the real estate. In figure 11 the module calculates the net present value of a
difference of a lawyer firm with an annual income of $1,500,0000, annual fix
cost of $300,0000, tax rate of %40, building value of $700,0000, building life
of 25 years, resale value of $270,000, average cost of capital of %6, estimated
annual rent of $60,000, estimated annual operating cost of $20,000. The firm rents
an office of his building to an accountant for $5,000 a year. For this example
the module calculates the difference at -$283,183, for which it suggests to
sell the property and rent a equivalent building since the sell will add net
present value to the firm.
- Rekomendasi :
Several limitations
were encountered as the availability of reliable data and the little research
in the topic. Artificial intelligence can be applied but with caution since
only few variables are publicly available to feed the models and it seems that
is not enough to get results reliable enough to replace good judgment. For the
other hand the Findlay portfolio management theory gave good results, the same
for financing and performance modules although there was not enough time to
measure performance given time constraints.
Finally there is a
need for more research in the topic, since there is great opportunity to
implement decision support system in the industry given the great benefits that
were presented in this research.
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