Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near‐term forecasts of water
This content will become publicly available on August 27, 2024
With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain‐aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap.
more » « less- Award ID(s):
- 2404946
- NSF-PAR ID:
- 10481161
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Risk Analysis
- ISSN:
- 0272-4332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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SUMMARY Operational earthquake forecasting for risk management and communication during seismic sequences depends on our ability to select an optimal forecasting model. To do this, we need to compare the performance of competing models in prospective experiments, and to rank their performance according to the outcome using a fair, reproducible and reliable method, usually in a low-probability environment. The Collaboratory for the Study of Earthquake Predictability conducts prospective earthquake forecasting experiments around the globe. In this framework, it is crucial that the metrics used to rank the competing forecasts are ‘proper’, meaning that, on average, they prefer the data generating model. We prove that the Parimutuel Gambling score, proposed, and in some cases applied, as a metric for comparing probabilistic seismicity forecasts, is in general ‘improper’. In the special case where it is proper, we show it can still be used improperly. We demonstrate the conclusions both analytically and graphically providing a set of simulation based techniques that can be used to assess if a score is proper or not. They only require a data generating model and, at least two forecasts to be compared. We compare the Parimutuel Gambling score’s performance with two commonly used proper scores (the Brier and logarithmic scores) using confidence intervals to account for the uncertainty around the observed score difference. We suggest that using confidence intervals enables a rigorous approach to distinguish between the predictive skills of candidate forecasts, in addition to their rankings. Our analysis shows that the Parimutuel Gambling score is biased, and the direction of the bias depends on the forecasts taking part in the experiment. Our findings suggest the Parimutuel Gambling score should not be used to distinguishing between multiple competing forecasts, and for care to be taken in the case where only two are being compared.
