The research at the intersection of climate science and
artificial intelligence (AI) is a dynamic and transformative field with
profound implications for sustainable development. Leveraging AI technologies
in climate and sustainability research involves the application of machine
learning algorithms to analyse vast and complex datasets, providing valuable
insights into climate patterns, environmental changes, and potential solutions.
These AI-driven approaches facilitate more accurate modelling of climate
phenomena, prediction of extreme events, and optimisation of resource
management strategies. Moreover, AI contributes to the development of
innovative tools for monitoring and mitigating environmental impact, enabling
real-time decision-making in areas such as renewable energy, conservation, and
climate adaptation. As technology continues to advance, the synergy between AI
and climate science holds the promise of revolutionising our understanding of
ecological systems and fostering more effective, data-driven strategies to
address the urgent challenges posed by climate change.
In the pursuit of understanding and addressing climate
change and sustainability, the integration of citizen-centric AI stands as a
pioneering avenue of research. This approach will harness the power of AI
technologies to actively involve and empower individuals within their
communities. By amalgamating citizen-generated data, AI algorithms, and
participatory platforms, researchers can cultivate a more inclusive and
responsive framework for sustainable development. This methodology not only
cultivates a deeper understanding of local environmental dynamics but also
fosters community engagement and ownership in crafting viable solutions.
Through AI-driven analyses of diverse data streams, ranging from crowd-sourced
environmental observations to individual lifestyle patterns, this research
champions a collaborative model that amplifies the voices of citizens,
fostering a collective effort towards a more sustainable and resilient future.
Bridging the gap between technical advancements and the
understanding of citizens is a challenge. Communicating AI-driven insights in
an accessible manner and creating user-friendly interfaces for diverse
audiences is crucial for the effective use of AI in promoting sustainability. Building
and maintaining community trust, engagement, and collaboration are key aspects.
Empowering citizens to actively participate in sustainability efforts and
ensuring that their contributions are valued are critical for the success of
citizen-centric AI initiatives. Implementing sustainable solutions based on AI
insights requires a consideration of long-term societal and environmental
impacts. Researchers must assess the durability and effectiveness of
interventions and technologies over time.
Experts
- Professor
Wai Lok Woo
- Professor Longzhi
Yang
- Professor
Shawn Lawson
- Dr Kyle Montague
Related Peak of Research Excellence
- Future of
Ice on Earth
- Energy Materials
& Systems
Related Projects at
Northumbria
- Next Generation of Flood Resilience
- Deep Learning and Explainable
Artificial Intelligence for Flood Prediction
- Forecasting and Studying Ocean-induced
Ice-Shelf Melt Rates using Machine Learning
- Machine Learning for Urban Climate:
How to Cool Down Your City with Trees
- Re-conceptualisation of Sustainable
Drainage Systems as Agents of Urban Development
- Artificial Intelligence used to Predict
Space Weather
Suggested Literature
- J. Sanderson, N. Tengtrairat, W. L. Woo, H. Mao, R.R. Al-Nima, “XFIMNet: An Explainable Deep Learning Architecture for Versatile Flood Inundation Mapping with Synthetic Aperture Radar and Multi-Spectral Optical Imagery,” International Journal of Remote Sensing, vol. 44, no. 24, pp. 7755-7789, 2023
- J. Sanderson, H. Mao, M.A.M. Abdullah, R.R.O. Al-Nima, W.L. Woo, “Optimal Fusion of Multispectral Optical and SAR Images for Flood Inundation Mapping through Explainable Deep Learning,” Information 2023, 14(12), 60
- S. Rosier, S. Bull, W.L. Woo, Hilmar, “Predicting Ocean-Induced Ice-Shelf Melt Rates using Deep Learning,” The Cryosphere, 17, 499–518, https://doi.org/10.5194/tc-17-499-2023, 2023.
- N. Tengtrairat, W.L. Woo, P. Parathai, C. Aryupong, P Jitsangiam, D. Rinchumphu, “Automated Landslide-Risk Prediction using Web GIS and Machine Learning Models,” Sensors 2021, 21(13), 4620
- E. Rollason, L.J. Bracken, R.J. Hardy, A.R.G. Large, “The importance of volunteered geographic information for the validation of flood inundation models,” Journal of Hydrology, 562, pp. 267-280, 2018
*if you are struggling
to access any of the suggested literature, then please contact CCAI@northumbria.ac.uk.
