AI for Anticipatory Action: Moving Beyond Climate Forecasting
- URL: http://arxiv.org/abs/2307.15727v1
- Date: Fri, 28 Jul 2023 17:32:59 GMT
- Title: AI for Anticipatory Action: Moving Beyond Climate Forecasting
- Authors: Benjamin Q. Huynh and Mathew V. Kiang
- Abstract summary: Disaster response agencies have been shifting from a paradigm of climate forecasting towards one of anticipatory action.
Machine learning models are becoming exceptionally powerful at climate forecasting.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Disaster response agencies have been shifting from a paradigm of climate
forecasting towards one of anticipatory action: assessing not just what the
climate will be, but how it will impact specific populations, thereby enabling
proactive response and resource allocation. Machine learning models are
becoming exceptionally powerful at climate forecasting, but methodological gaps
remain in terms of facilitating anticipatory action. Here we provide an
overview of anticipatory action, review relevant applications of machine
learning, identify common challenges, and highlight areas where machine
learning can uniquely contribute to advancing disaster response for populations
most vulnerable to climate change.
Related papers
- Robustness of AI-based weather forecasts in a changing climate [1.4779266690741741]
We show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states.
Despite current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science.
arXiv Detail & Related papers (2024-09-27T08:11:49Z) - Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks.
We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events.
Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
arXiv Detail & Related papers (2024-09-11T19:31:56Z) - Performative Prediction on Games and Mechanism Design [69.7933059664256]
We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy.
As predictions shape collective outcomes, social welfare arises naturally as a metric of concern.
We show how to achieve better trade-offs and use them for mechanism design.
arXiv Detail & Related papers (2024-08-09T16:03:44Z) - Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
We present an AI-based data assimilation model, i.e., Adas, for global weather variables.
We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term.
We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
arXiv Detail & Related papers (2023-12-18T09:05:28Z) - CMIP X-MOS: Improving Climate Models with Extreme Model Output
Statistics [40.517778024431244]
We introduce Extreme Model Output Statistics (X-MOS) to improve predictions of natural disaster risks.
This approach utilizes deep regression techniques to precisely map CMIP model outputs to real measurements obtained from weather stations.
In contrast to previous research, our study places a strong emphasis on enhancing the estimation of the tails of future climate parameter distributions.
arXiv Detail & Related papers (2023-10-24T13:18:53Z) - What Should I Know? Using Meta-gradient Descent for Predictive Feature
Discovery in a Single Stream of Experience [63.75363908696257]
computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations.
An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making.
We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
arXiv Detail & Related papers (2022-06-13T21:31:06Z) - ClimateGAN: Raising Climate Change Awareness by Generating Images of
Floods [89.61670857155173]
We present our solution to simulate photo-realistic floods on authentic images.
We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
arXiv Detail & Related papers (2021-10-06T15:54:57Z) - Remote sensing, AI and innovative prediction methods for adapting cities
to the impacts of the climate change [0.0]
I propose an AI-based framework which might be useful for extracting indicators from remote sensing images.
I underline that this is an open field and an ongoing research for many scientists, therefore I offer an in depth discussion on the challenges and limitations of AI-based methods.
arXiv Detail & Related papers (2021-07-06T15:55:26Z) - Heterogeneous-Agent Trajectory Forecasting Incorporating Class
Uncertainty [54.88405167739227]
We present HAICU, a method for heterogeneous-agent trajectory forecasting that explicitly incorporates agents' class probabilities.
We additionally present PUP, a new challenging real-world autonomous driving dataset.
We demonstrate that incorporating class probabilities in trajectory forecasting significantly improves performance in the face of uncertainty.
arXiv Detail & Related papers (2021-04-26T10:28:34Z) - The Power of Language: Understanding Sentiment Towards the Climate
Emergency using Twitter Data [0.0]
It could be speculated that there is a relationship between Crude Oil Futures and sentiment towards the Climate Emergency.
This study shows that it is possible to split the conversation surrounding the Climate Emergency into 3 distinct topics.
arXiv Detail & Related papers (2021-01-25T19:51:10Z) - The Human Effect Requires Affect: Addressing Social-Psychological
Factors of Climate Change with Machine Learning [2.0178765779788495]
We propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change.
We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma.
We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted.
arXiv Detail & Related papers (2020-11-24T23:34:54Z)
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.