ABSTRACT The use of artificial intelligence (AI) and machine learning (ML) methods in the geosciences can be categorized into three types, those that: (1) accelerate computationally expensive Earth system models; (2) fill the vacuum where numerical and physics-based models struggle; and (3) enable and enlighten data-driven discoveries. To achieve these tasks, many cyberinfrastructure (CI) systems are required. This chapter reviews the cutting-edge CI aiding the implementation of AI in the geosciences. Each technique presented is evaluated to assist geoscientists in determining how appropriate it is. Use cases in the subdomains of seismology, hydrology, and climatology are introduced to help readers understand the workflows. Challenges and future opportunities for CI development center on big data, provenance, interoperability, and heterogeneity due to the scale and complexity that future AI models in the geosciences will require.

Abstract Thailand is one of the many countries in Asia that fell victim to the monstrous 2004 Indian Ocean tsunami. To date, there has been tons of effort to reduce disaster risk in Thailand. Nevertheless, there is still a considerable margin for improvement. This paper aims to review previous efforts and measures that have been taken to reduce Thailand's risk to disasters, and highlight possible areas for improvement. We use the disaster risk management as a thought-framework for our review, where we discuss Thailand's current disaster risk reduction (DRR) measures from a multi-perspective: the tsunami numerical simulation, tsunami warning system, tsunami evacuation sign, manuals and guidelines, evacuation shelters, housing recovery, hotel business and recovery, public awareness and media, and disaster education. In understanding the current state of disaster risk efforts, we will then highlight potential areas for improvement.