An intelligent flood risk assessment system using belief rule base
Hridoy, Md Rafiul Sabbir (2017)
Diplomityö
Hridoy, Md Rafiul Sabbir
2017
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe201708298252
https://urn.fi/URN:NBN:fi-fe201708298252
Tiivistelmä
Natural disasters disrupt our daily life and cause many sufferings. Among the various
natural disasters, flood is one of the most catastrophic. Assessing flood risk helps
to take necessary precautions and can save human lives. The assessment of risk
involves various factors which can not be measured with hundred percent certainty.
Therefore, the present methods of flood risk assessment can not assess the risk of
flooding accurately.
This research rigorously investigates various types of uncertainties associated with
the flood risk factors. In addition, a comprehensive study of the present flood risk
assessment approaches has been conducted. Belief Rule Base expert systems are
widely used to handle various of types of uncertainties. Therefore, this research
considers BRBES’s approach to develop an expert system to assess the risk of flooding.
In addition, to facilitate the learning procedures of BRBES, an optimal learning
algorithm has been proposed. The developed BRBES has been applied taking real
world case study area, located at Cox’s Bazar, Bangladesh. The training data has
been collected from the case study area to obtain the trained BRB and to develop
the optimal learning model. The BRBES can generate different "What-If" scenarios
which enables the analysis of flood risk of an area from various perspectives which
makes the system robust and sustainable. This system is said to be intelligent as it
has knowledge base, inference engine as well as the learning capability.
natural disasters, flood is one of the most catastrophic. Assessing flood risk helps
to take necessary precautions and can save human lives. The assessment of risk
involves various factors which can not be measured with hundred percent certainty.
Therefore, the present methods of flood risk assessment can not assess the risk of
flooding accurately.
This research rigorously investigates various types of uncertainties associated with
the flood risk factors. In addition, a comprehensive study of the present flood risk
assessment approaches has been conducted. Belief Rule Base expert systems are
widely used to handle various of types of uncertainties. Therefore, this research
considers BRBES’s approach to develop an expert system to assess the risk of flooding.
In addition, to facilitate the learning procedures of BRBES, an optimal learning
algorithm has been proposed. The developed BRBES has been applied taking real
world case study area, located at Cox’s Bazar, Bangladesh. The training data has
been collected from the case study area to obtain the trained BRB and to develop
the optimal learning model. The BRBES can generate different "What-If" scenarios
which enables the analysis of flood risk of an area from various perspectives which
makes the system robust and sustainable. This system is said to be intelligent as it
has knowledge base, inference engine as well as the learning capability.