Background of Study for Online Hotel Reservation System
SAMPLE PROJECT PROPOSAL
FINAL YEAR PROJECT PROPOSAL
SUBMITTED TO
THE FACULTY OF INFORMATICS’ PROJECT COMMITTEE,
GTUC
TITLE:
An artificial neural network (ANN) approach to rainfall-runoff modelling
PROJECT TYPE:
Evaluation & development project
AUTHOR(S):
KWAME GYASI – 12345
KWABENA JONES – 67899
DATE:
28TH FEBRUARY, 2012
Background
The United Nations General Assembly declared the 1990s the International Decade for Natural Disaster Reduction with the specific intent to disseminate existing and new information related to measures for the assessment, prediction, prevention and mitigation of natural disasters (WMO, 1992). A prominent element within this program has been the development of operational flood forecasting systems.
These systems have evolved through advances in mathematical modelling (Wood and O’Connell, 1985; O’Connell, 1991; Lamberti and Pilati, 1996), the installation of telemetry and field monitoring equipment at critical sites in drain¬age networks (Alexander, 1991), through satellite and radar sensing of extreme rainfalls (Collier, 1991), and through the coupling of precipitation and runoff models (Georgakakos and Foufoula-Georgiou, 1991; Franchini et al., 1996).
However, in practice, successful real-time flood forecasting often depends on the efficient integration of all these separate activities (Douglas and Dobson, 1987). Under the auspices of the World Meteorological Organization (1992) a series of projects were implemented to compare the characteristics and performance of various operational models and their updating procedures. A major conclusion of the most recent inter-comparison exercise was the need for robust simulation models in order to achieve consistently better results for longer lead times even when accompanied by an efficient updating procedure.
The attractiveness of Artificial Neural Networks (ANNs) to flood forecasting is threefold. First, ANNs can represent any arbitrary non-linear
FINAL YEAR PROJECT PROPOSAL
SUBMITTED TO
THE FACULTY OF INFORMATICS’ PROJECT COMMITTEE,
GTUC
TITLE:
An artificial neural network (ANN) approach to rainfall-runoff modelling
PROJECT TYPE:
Evaluation & development project
AUTHOR(S):
KWAME GYASI – 12345
KWABENA JONES – 67899
DATE:
28TH FEBRUARY, 2012
Background
The United Nations General Assembly declared the 1990s the International Decade for Natural Disaster Reduction with the specific intent to disseminate existing and new information related to measures for the assessment, prediction, prevention and mitigation of natural disasters (WMO, 1992). A prominent element within this program has been the development of operational flood forecasting systems.
These systems have evolved through advances in mathematical modelling (Wood and O’Connell, 1985; O’Connell, 1991; Lamberti and Pilati, 1996), the installation of telemetry and field monitoring equipment at critical sites in drain¬age networks (Alexander, 1991), through satellite and radar sensing of extreme rainfalls (Collier, 1991), and through the coupling of precipitation and runoff models (Georgakakos and Foufoula-Georgiou, 1991; Franchini et al., 1996).
However, in practice, successful real-time flood forecasting often depends on the efficient integration of all these separate activities (Douglas and Dobson, 1987). Under the auspices of the World Meteorological Organization (1992) a series of projects were implemented to compare the characteristics and performance of various operational models and their updating procedures. A major conclusion of the most recent inter-comparison exercise was the need for robust simulation models in order to achieve consistently better results for longer lead times even when accompanied by an efficient updating procedure.
The attractiveness of Artificial Neural Networks (ANNs) to flood forecasting is threefold. First, ANNs can represent any arbitrary non-linear
References: Alexander, B. (1991). The implications of science (2nd Edition), Pitman Publishing, London. Collier, D. (1991). Adaptation of genetic algorithms in Hawthorne Analysis’, Management Monthly, Vol 28(2), pp 21–23.