Harnessing Machine Learning and Deep Learning applications for climate change risk assessment: a survey

In the last years there has been a growing interest around Machine Learning (ML) in climate risk/ multi-risk assessment, steered mainly by the growing amount of data available and the reduction of associated computational costs. Extracting information from spatio-temporal data is critically important for problems such as extreme events forecasting and assessing risks and impacts from multiple hazards. Typical challenges in which AI and ML are now being applied require understanding the dynamics of complex systems, which involve many features with non-linear relations and feedback loops, analysing the effects of phenomena happening at different time scales, such as slow-onset events (sea level rise) and short-term episodic events (storm surges, floods) and estimating uncertainties of long-term predictions and scenarios. 
While in the last years there were many successful applications of AI/ML, such as Random Forest or Long-Short Term Memory (LSTM) in floods and storm surges risk assessment, there are still open questions and challenges that need to be addressed. In fact, there is a lack of data for extreme events and Deep Learning (DL) algorithms often need huge amounts of information to disentangle the relationships among hazard, exposure and vulnerability factors contributing to the occurrence of risks. Moreover, the spatio-temporal resolution can be highly irregular and need to be reconstructed to produce accurate and efficient models. For example, using data from meteorological ground stations can offer accurate datasets with fine temporal resolution, but with an irregular distribution in the spatial dimension; on the other hand, leveraging on satellite images can give access to more spatially refined data, but often lacking the temporal dimension (fewer events available to due atmospheric disturbances). 
Several techniques have been applied, ranging from classical multi-step forecasting, state-space and Hidden Markov models to DL techniques, such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). ANN and Deep Generative Models (DGM) have been used to reconstruct spatio-temporal grids and modelling continuous time-series, CNN to exploit spatial relations, Graph Neural Networks (GNN) to extract multi-scale localized spatial feature and RNN and LSTM for multi-scale time series prediction.  
To bridge these gaps, an in-depth state-of-the-art review of the mathematical and computer science innovations in ML/DL techniques that could be applied to climate /multi-risk assessment was undertaken. The review focuses on three possible ML/DL applications: analysis of spatio-temporal dynamics of risk factors, with particular attention on applications for irregular spatio-temporal grids; multivariate analysis for multi-hazard interactions and multiple risk assessment endpoints; analysis of future scenarios under climate change. We will present the main outcomes of the scientometric and systematic review of publications across the 2000- 2021 timeframe, which allowed us to: i) summarize keywords and word co-occurrence networks, ii) highlight linkages, working relations and co-citation clusters, iii) compare ML and DL approaches with classical statistical techniques and iv) explore applications at the forefront of the risk assessment community.