Problem-Solving using Computational Thinking :
Real-world Application
The objective of this project is to apply Computational Thinking (CT) principles to analyze and address the challenges posed by potential natural disasters, specifically flooding, through predictive modeling and mitigation strategies. By leveraging decomposition, pattern recognition, abstraction, and algorithmic thinking, this project aims to systematically assess flood risks and implement proactive solutions to safeguard local communities.​
This project seeks to:
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Identify Key Risk Factors – Assess environmental, infrastructural, and demographic factors contributing to flood vulnerability, including heavy rainfall patterns, drainage inefficiencies, and the presence of at-risk populations such as the elderly.
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Analyze the Impact of Urban Development – Examine how ongoing construction projects, roadblocks, and altered drainage systems may exacerbate flooding risks, disrupting mobility and emergency response efforts.
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Develop a Predictive Flood Model – Utilize historical climate data, rainfall trends, and topographical mapping to anticipate flood-prone areas and assess their potential impact on daily life activities, especially for elderly residents.
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Implement Targeted Mitigation Strategies – Design algorithmic solutions to improve flood response, such as optimized drainage planning, early warning alert systems, and infrastructure adaptations to accommodate both ongoing construction and emergency access needs.
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Enhance Community Safety Measures – Ensure that solutions prioritize the protection of vulnerable populations, including elderly individuals in retirement communities who rely on accessible pathways, road networks, and timely disaster alerts.​​​​​​
Project Justification
It is always helpful to document how the core pillars of computational thinking is adopted in your project.​ The iterations can be observed in the graphic organizer to better understand the problem and thought process applied.
Problem Identification
As shown in my iterations starting from 1, I kicked off with a key process of problem identification by identifying the problem on a vast scale highlighting basically the question of how and what approaches to take. However, digressing further to iterations, 2, 3, and 4, the identified problems were codified in detail for better comprehension of the bogus problem identified in iteration 1. Moreso, from analysing the problems comprehensively and looking into some trends in the pattern recognition of iteration 1, insights were derived and the following upshots were drawn to position the stated problem to be better solved computationally.
They include:
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Evacuate the residents?
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Stop the construction?
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Commence preventive actions to rehabilitate the only accessible route which is flood prone?
Decomposition
The decomposition approach was exploited in iterations 1, 2, 3 and 4 as my stated problem needed to be fragmented into sub-problems so as to be computable. Hence, iteration 1 was decomposed into 6 sub-problems, iteration 2 (7 sub-problems), iteration 3(5 sub-problems), iteration 4 (3 sub-problems) respectively. The disintegration of the stated problem into the various sub problems gave a more clear understanding to better solve them quantitatively. The quantifiable sub-problems can be seen below in the graphic organizer.
Pattern Recognition
This facet was adopted in iteration 1 & 2. Crucial in diagnosing the trends in the identified problem which was a great deal of help to narrowing down the problem solutions by correlating past approaches and improving on them to combat the challenges posed in the identified problem.
Abstraction
As abstraction is an important step in solving problems computationally, it was however depicted in iteration 1, 2 and 3, showing that some sub-problems were irrelevant and not quantifiable factors in solving the identified problem. This method gave a focus to important factors that were used to build the algorithm.
Iterations
