Frost forecasting model using graph neural networks with spatio-temporal attention

International audienceFrost forecast is an important issue in climate research because of its economic impact in several industries. In this study, a graph neural network (GNN) with spatio-temporal architecture is proposed to predict minimum temperatures in an experimental site. The model considers spatial and temporal relations and processes multiple time series simultaneously. Performing predictions of 6, 12, and 24 hrs this model outperforms statistical and non-graph deep learning models

Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling

The carbon pump of the world's ocean plays a vital role in the biosphere and climate of the earth, urging improved understanding of the functions and influences of the ocean for climate change analyses. State-of-the-art techniques are required to develop models that can capture the complexity of ocean currents and temperature flows. This work explores the benefits of using physics-informed neural networks (PINNs) for solving partial differential equations related to ocean modeling; such as the Burgers, wave, and advection-diffusion equations. We explore the trade-offs of using data vs. physical models in PINNs for solving partial differential equations. PINNs account for the deviation from physical laws in order to improve learning and generalization. We observed how the relative weight between the data and physical model in the loss function influence training results, where small data sets benefit more from the added physics information

A Graph Neural Network with Spatio-temporal Attention for Multi-sources Time Series Data: An application to Frost Forecast

International audienceFrost forecast is an important issue in climate research because of its economic impact on several industries. In this study, we propose GRAST-Frost, a graph neural network (GNN) with spatio-temporal architecture, which is used to predict minimum temperatures and the incidence of frost. We developed an IoT platform capable of acquiring weather data from an experimental site, in addition data was collected from 10 weather stations in close proximity to the aforementioned site. The model considers spatial and temporal relations while processing multiple time series simultaneously. Performing predictions of 6, 12, 24 and 48 hours in advance, this model outperforms classical time series forecasting methods including, linear and non-linear machine learning methods, simple deep learning architectures and non-graph deep learning models. In addition, we show that our model significantly improves on the current state of the art of frost forecasting methods

Assessing Physics Informed Neural Networks in Ocean Modelling and Climate Change Applications

International audienceThe carbon pump of the world's oceans plays a vital role in the biosphere and climate of the earth, urging improved understanding of the functions and influences of the oceans for climate change analyses. State-of-the-art techniques are required to develop models that can capture the complexity of ocean currents and temperature flows. We will explore the benefits of using physics informed neural networks (PINNs) for solving partial differential equations related to ocean modeling; such as the wave, shallow water, and advection-diffusion equations. PINNs account for the deviation from physical laws in order to improve learning and generalization. However, in this work, we observe worse training and generalization results, possibly due the amount of data used in training

Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling

The carbon pump of the world's ocean plays a vital role in the biosphere and climate of the earth, urging improved understanding of the functions and influences of the ocean for climate change analyses. State-of-the-art techniques are required to develop models that can capture the complexity of ocean currents and temperature flows. This work explores the benefits of using physics-informed neural networks (PINNs) for solving partial differential equations related to ocean modeling; such as the Burgers, wave, and advection-diffusion equations. We explore the trade-offs of using data vs. physical models in PINNs for solving partial differential equations. PINNs account for the deviation from physical laws in order to improve learning and generalization. We observed how the relative weight between the data and physical model in the loss function influence training results, where small data sets benefit more from the added physics information

High-level information fusion for risk and accidents prevention in pervasive oil industry environments

Proceedings of: 12th International Conference on Practical Applications of Agents and Multi-Agent Systems, University of Salamanca (Spain), 4th-6th June, 2014.Information fusion studies theories and methods to effectively combine data from multiple sensors and related information to achieve more specific inferences that could be achieved by using a single, independent sensor. Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications. In this sense, the deployment of an Intelligent Offshore Oil Industry Environment will help to figure out a risky scenario based on the events occurred in the past related to anomalies and the profile of the current employee (role, location, etc.). In this paper we propose an information fusion model for an intelligent oil environment in which employees are alerted about possible risk situations while their are moving around their working place. The layered architecture, implements a reasoning engine capable of intelligently filtering the context profile of the employee (role, location) for the feature selection of an inter-transaction mining process. Depending on the employee contextual information he will receive intelligent alerts based on the prediction model that use his role and his current location. This model provides the big picture about risk analysis for that employee at that place in that moment.This work was partially funded by CNPq BJT Project 407851/2012-

A Roadmap for AI in Latin America

International audienceIf we want ensure that AI in the upcoming years is a positive factor of the development of Latin America we need to start acting now and stop doing the same thing over and over again. The recent past and the current context in the region clearly indicates that it is unlikely that we see any improvements in the resources and support that AI has, instead, it will probably be aggravated by the impact of the COVID-19 pandemic. Consequently, it is our role as researchers to visit this issue and attempt to propose a road map towards a solution.The driving motivation for this paper is to plant the seeds of a discussion on how to create a bottom-up and inclusive positive momentum for AI in the region, given the existing conditions, while, at the same time, reducing the potential negative impacts that it might have. We present this in the form of a roadmap or workflow that identifies the main obstacles that should be addressed and how they can be overcome by a combination focusing the work AI practitioners on particular research topics and that of decision markers and concern citizens

Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change

International audienceThe ongoing transformation of climate and biodiversity will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand and quantify the consequences of these perturbations on the marine ecosystem. Understanding this phenomenon is not only an urgent but also a scientifically demanding task. Consequently, it is a problem that must be addressed with a tific cohort approach, where multi-disciplinary teams collaborate to bring the best of different scientific areas. In this proposal paper, we describe our newly launched four-years project focusedon developing new artificial intelligence, machine learning, and mathematical modeling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome and its relation with climate change. These actions should enable the understanding of our oceans and predict and mitigate the consequences of climate and biodiversity changes