Karami Lawal, Z.; Zakari, R.Y.; Yassin, H. Design of Smart Flood Risk Management System: A Brunei Darussalam Vision 2035 (WAWASAN 2035) for Climate Resilience and Adaptation. Preprints2024, 2024021679. https://doi.org/10.20944/preprints202402.1679.v1
APA Style
Karami Lawal, Z., Zakari, R.Y., & Yassin, H. (2024). Design of Smart Flood Risk Management System: A Brunei Darussalam Vision 2035 (WAWASAN 2035) for Climate Resilience and Adaptation. Preprints. https://doi.org/10.20944/preprints202402.1679.v1
Chicago/Turabian Style
Karami Lawal, Z., Rufai Yusuf Zakari and Hayati Yassin. 2024 "Design of Smart Flood Risk Management System: A Brunei Darussalam Vision 2035 (WAWASAN 2035) for Climate Resilience and Adaptation" Preprints. https://doi.org/10.20944/preprints202402.1679.v1
Abstract
Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.
Keywords
Big Data; Climate Change; Flood Modelling; Internet of Things; Machine Learning; Risk Management
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
