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Literature review: Machine Learning problem for Climate
The “Ten Problems for Climate in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the ten problems that we are going to introduce in this booklet:
- energy policies,
- machine learning,
- non-state actors,
- social sciences,
- green infrastructures,
- regional environments,
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
3 Machine Learning
THE PROBLEM — Despite the growth of movements applying Machine Learning and Artificial Intelligence to problems of societal and global good, there remains the need for a concerted effort to identify how these tools may best be applied to tackle climate change. Machine Learning is now expanding into climate research, with the goal of reducing the uncertainty in climate models, specifically as it relates to climate sensitivity and predicting regional trends. Strategies to tackle environmental concerns, though, have been slow to evolve while the speed of the impending crisis only intensifies.
CASE STUDIES — … buy this booklet from Amazon …
CONCLUSIONS — From smart grids to disaster management, researchers identify high impact problems where existing gaps can be filled by Machine Learning, in collaboration with other fields. More accurate and fast climate models are needed to be run for more than a thousand years just to get to them into the current climate state before then going forward into future climates. By analyzing large quantities of data that is generated on a frequent basis from sensors, gauges, and monitors located all over the world, Artificial Intelligence can spot patterns quickly and automatically, painting a very accurate picture for scientists as to how our planet is changing. It can even perform sentiment analysis to assess the influence of activists such as Greta Thunberg. That said, optimizing tasks for productivity or profit alone is not directly tied to reducing greenhouse gas emissions. Deep neural networks can also be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. Environmental impact of aviation can be reduced by considering machine learning solutions that would reduce the flight time. They can also help establish the historical streamflow response to climate change and forecast the future response in mountainous watersheds. Using a neural network trained on widely available weather forecasts and historical turbine data, the DeepMind system by Google predicts wind power output 36 hours ahead of actual generation. The COVID-19 pandemic has shown that renewable electricity because of its decentralized nature seems more suitable for the situation. Video prediction with remote sensing can be leveraged to monitor and forecast forest change at high resolution.
TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …
booklet updated on 13 Dec 2020, now on sale as version 1.1