This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series from all geoscientific disciplines.

Methods to be discussed include, but are not limited to:
- linear and nonlinear methods of time series analysis
- time-frequency methods
- predictive approaches
- statistical inference for nonlinear time series
- nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data
- nonlinear correlation analysis and synchronisation
- surrogate data techniques
- filtering approaches and nonlinear methods of noise reduction
- artificial intelligence and machine learning based analysis and prediction for univariate and multivariate time series

Contributions on methodological developments and applications to problems across all geoscientific disciplines are equally encouraged.

This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, deep learning and Artificial Intelligence applications in geosciences
• Visualization and visual analytics of big and high-dimensional data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

Machine learning (ML) is now widely used across the Earth Sciences and especially its subfield deep learning (DL) has recently enjoyed increased attention in the context of Hydrology. The goal of this session is to highlight the continued integration of ML, and DL in particular, into traditional and emerging Hydrology-related workflows. Abstracts are solicited related to novel theory development, novel methodology, or practical applications of ML and DL in Hydrology. This might include, but is not limited to, the following:

(1) Identifying novel ways for DL in hydrological modelling.
(2) Testing and examining the usability of DL based approaches in hydrology.
(3) Improving understanding of the (internal) states/representations of DL models.
(4) Integrating DL with traditional hydrological models.
(5) Creating an improved understanding of the conditions for which DL provides reliable simulations. Including quantifying uncertainty in DL models.
(6) Clustering and/or classifying hydrologic systems, events and regimes.
(7) Using DL for detecting, quantifying or cope with nonstationarity in hydrological systems and modeling.
(8) Deriving scaling relationships or process-related insights directly from DL.
(8) Using DL to model or anticipate human behavior or human impacts on hydrological systems.
(10) DL based hazard analysis, detection/mitigation, event detection, etc.
(11) Natural Language Processing to analyze, interpret, or condense hydrologically-relevant peer-reviewed literature or social media data or to assess trends within the discipline.

Smart monitoring and observation systems for natural hazards, including satellites, seismometers, global networks, unmanned vehicles (e.g., UAV), and other linked devices, have become increasingly abundant. With these data, we observe the restless nature of our Earth and work towards improving our understanding of natural hazard processes such as landslides, debris flows, earthquakes, floods, storms, and tsunamis. The abundance of diverse measurements that we have now accumulated presents an opportunity for earth scientists to employ statistically driven approaches that speed up data processing, improve model forecasts, and give insights into the underlying physical processes. Such big-data approaches are supported by the wider scientific, computational, and statistical research communities who are constantly developing data science and machine learning techniques and software. Hence, data science and machine learning methods are rapidly impacting the fields of natural hazards and seismology. In this session, we will see research from natural hazards and seismology for processes over a broad range of time and spatial scales.

Dr. Pui Anantrasirichai of the University of Bristol, UK will give the invited presentation:
Application of Deep Learning to Detect Ground Deformation in InSAR Data

There are many ways in which machine learning promises to provide insight into the Earth System, and this area of research is developing at a breathtaking pace. If unsupervised, supervised as well as reinforcement learning can hold this promise remains an open question, particularly for predictions. Machine learning could help extract information from numerous Earth System data, such as satellite observations, as well as improve model fidelity through novel parameterisations or speed-ups. This session invites submissions spanning modelling and observational approaches towards providing an overview of the state-of-the-art of the application of these novel methods.

Most of the processes studied by geoscientists are characterized by variations in both space and time. These spatio-temporal phenomena have been traditionally investigated using linear statistical approaches, as in the case of physically-based models and geostatistical models. Additionally, the rising attention toward machine learning, as well as the rapid growth of computational resources, opens new horizons in understanding, modelling and forecasting complex spatio-temporal systems through the use of stochastics non-linear models.
This session aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal geostatistics or data mining on topics that include, but are not limited to:
- advances in spatio-temporal modeling using geostatistics and machine learning;
- uncertainty quantification and representation;
- innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.
The main applications will be closely related to the research in environmental sciences and quantitative geography. A non-complete list of possible applications includes:
- natural and anthropogenic hazards (e.g. floods; landslides; earthquakes; wildfires; soil, water, and air pollution);
- interaction between geosphere and anthroposphere (e.g. land degradation; urban sprawl);
- socio-economic sciences, characterized by the spatial and temporal dimension of the data (e.g. census data; transport; commuter traffic).