Towards the Development of a Capability Assessment System for Flood Risk Management

Having in place adequate levels of emergency management capabilities (EMCs) underpins a managed civil emergency response, especially during a flooding event(s). Good EMC is either built on having the right internal capabilities or by exploiting existing emergency capabilities from other responders. In some countries, such as Saudi Arabia, there is a noted lack of decision‐making in the Civil Defence (CD) Authority about generating effective mutual‐aid requests. Three core areas of EMC include having the right types and levels of response equipment to hand, ensuring sufficient Human Resources, can be maintained throughout a sustained event, and developing adequate Training capabilities. Other factors impacting on Saudi Arabia include both stress and a lack of work experience. In this chapter, we examine the effectiveness of a prototype IT System in the case of Saudi CD Authority as a tool for addressing the availability and adequacy of mutual‐aid for EMC, Human Resources (HR), and training capabilities against scalable levels of flood risk event(s). The proposed IT System is built using the ‘fuzzy expert system’ approach

Detecting longitudinal patterns of daily smoking following drastic cigarette reduction

To enhance prolonged smoking cessation or reduction, a better understanding of the process of change is needed. This study examines daily smoking rates following the end of an intensive smoking reduction program originally designed to evaluate the relationship of tobacco biomarkers with reduced levels of smoking. A novel pattern-oriented approach called time series-based typology is used to detect homogeneous smoking patterns in time-intensively (i.e., 40 occasions) observed smokers (n = 57), who were predominantly Caucasian (94.7%), male (52.6%), and on average 47.9 years old (SD = 11.3). The majority of the smokers exhibited a change in their daily smoking behavior over the course of 40 days with 47.4% increasing and 40.4% decreasing the number of cigarettes smoked per day, which is contrary to the results a group level approach would have found. Very few smokers (12.3%) maintained their average smoking rate, and exhibited an externally controlled smoking pattern. Trajectory type could be predicted by temporally proximal motivation and self-efficacy variables ((F(4, 106) =3.46, p = .011, η2 = .115), underscoring their importance in maintaining reduced smoking rates. Time series-based typology demonstrated good sensitivity to the identification of meaningfully different trajectories

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society