Climate and Sustainability

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

• Improving Flood Risk Management in Small, Rapid Response Catchments Under Climate Change

• 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.cdt@northumbria.ac.uk