ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach

• URL: http://arxiv.org/abs/2012.00671v2
• Date: Sun, 6 Dec 2020 02:26:36 GMT
• Title: ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
• Authors: Sella Nevo, Gal Elidan, Avinatan Hassidim, Guy Shalev, Oren Gilon, Grey Nearing, Yossi Matias
• Abstract summary: Floods are among the most common and deadly natural disasters in the world. Yet the majority of the world's vulnerable population does not have access to reliable and actionable warning systems. In this paper we present two components of flood forecasting systems which were developed over the past year.
• Score: 17.839074983736467
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the world's vulnerable population does not have access to reliable and actionable warning systems, due to core challenges in scalability, computational costs, and data availability. In this paper we present two components of flood forecasting systems which were developed over the past year, providing access to these critical systems to 75 million people who didn't have this access before.

Related papers

• Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting [7.4807361562214405]
  Floods are among the most common and devastating natural hazards. Recent progress in weather prediction and spaceborne flood mapping demonstrated the feasibility of anticipating extreme events. There is a critical lack of datasets and benchmarks to enable the direct forecasting of flood extent.
  arXiv  Detail & Related papers  (2024-09-27T09:51:25Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• AI Increases Global Access to Reliable Flood Forecasts [6.498815455353096]
  Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
  arXiv  Detail & Related papers  (2023-07-30T01:49:21Z)
• Towards Generalizable Data Protection With Transferable Unlearnable Examples [50.628011208660645]
  We present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution.
  arXiv  Detail & Related papers  (2023-05-18T04:17:01Z)
• Transformer-based Flood Scene Segmentation for Developing Countries [1.7499351967216341]
  Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil. Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize damage. FloodTransformer is the first visual transformer-based model to detect and segment flooded areas from aerial images at disaster sites.
  arXiv  Detail & Related papers  (2022-10-09T10:29:41Z)
• Flood Prediction Using Machine Learning Models [0.0]
  This paper aims to reduce the extreme risks of this natural disaster by providing a prediction for floods using different machine learning models. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.
  arXiv  Detail & Related papers  (2022-08-02T03:59:43Z)
• PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities [0.32228025627337864]
  We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling.
  arXiv  Detail & Related papers  (2021-12-26T23:48:23Z)
• Certifiers Make Neural Networks Vulnerable to Availability Attacks [70.69104148250614]
  We show for the first time that fallback strategies can be deliberately triggered by an adversary. In addition to naturally occurring abstains for some inputs and perturbations, the adversary can use training-time attacks to deliberately trigger the fallback. We design two novel availability attacks, which show the practical relevance of these threats.
  arXiv  Detail & Related papers  (2021-08-25T15:49:10Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• Overcoming Failures of Imagination in AI Infused System Development and Deployment [71.9309995623067]
  NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure" We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
  arXiv  Detail & Related papers  (2020-11-26T18:09:52Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.