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Tackling Climate Change with Machine Learning [article]

David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli (+6 others)
2019 arXiv   pre-print
We call on the machine learning community to join the global effort against climate change.  ...  Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.  ...  The authors gratefully acknowledge support from National Science Foundation grant 1803547, the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science  ... 
arXiv:1906.05433v2 fatcat:ykmqsivkbfcazaz3wl5f7srula

Tackling climate change with machine learning [article]

David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj (+11 others)
2022
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help.  ...  We call on the ML community to join the global effort against climate change.  ...  Tackling Climate Change with Machine Learning 42:7 Fig. 1 .  ... 
doi:10.14279/depositonce-15739 fatcat:i3bxf2hmn5hcpfswamncwwdcla

Early Experiences on Machine Learning for Climate Change Applications at CMCC

Gabriele Accarino, Donatello Elia, Sandro Fiore, Giovanni Aloisio
2019 Zenodo  
Presentation about "Early Experiences on Machine Learning for Climate Change Applications at CMCC" given by G. Accarino and D.  ...  Elia at the Workshop on Machine Learning for Weather and Climate Modelling, 2-5 September 2019, Oxford, UK  ...  There is a strong interest in combining climate science with approaches from statistics, analytics, big data, machine learning and mining.  ... 
doi:10.5281/zenodo.3973013 fatcat:c6qomjj6cnfedalrp5qbiiovea

Situating AI on the road from data sharing to societal impact

Daniel Mietchen
2020 Zenodo  
we can do about it ⬡ Tackling Climate Change with Machine Learning ⬡ How artificial intelligence can tackle climate changeClimate Change AI Addressing the climate emergency (iii) AI_techniques + faster  ...  Addressing the climate emergency (ii) ⬡ Here are 10 ways AI could help fight climate changeClimate change and machine learning ⬡ How AI Is Helping Solve Climate Change ⬡ Fighting climate change with  ... 
doi:10.5281/zenodo.3996019 fatcat:yttxelvs7fdftlgaml4m4g5fvi

The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning [article]

Kyle Tilbury, Jesse Hoey
2020 arXiv   pre-print
Machine learning has the potential to aid in mitigating the human effects of climate change.  ...  Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it.  ...  We propose that machine learning based approaches that contend with humans and climate change must incorporate affect.  ... 
arXiv:2011.12443v1 fatcat:icfnvxpxqbfhvatcw7amhpjwzm

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

Bogdan Bochenek, Zbigniew Ustrnul
2022 Atmosphere  
research—parametrizations, extreme events, and climate change.  ...  In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using  ...  From the outset, it is worth citing a fundamental publication by 22 authors entitled 'Tackling Climate Change with Machine Learning' [73] , which includes a very wide spectrum of machine learning applications  ... 
doi:10.3390/atmos13020180 fatcat:c42iamvesfc67dpnldfq2nqfpq

Predicting ice flow using machine learning [article]

Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio
2019 arXiv   pre-print
We present a dataset, IceNet, to encourage machine learning research and to help facilitate further applications in the areas of cryospheric science and climate change.  ...  As the volume of cryosphere data increases in coming years, this is an interesting and important opportunity for machine learning to address a global challenge for climate change, risk management from  ...  Submitted to NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning. Correspondence to: S. Karthik Mukkavilli <mukkavis@mila.quebec>. Table 1.  ... 
arXiv:1910.08922v1 fatcat:2mkg3kjwereknmurnmqnrydrja

Capabilities of Universities in Achieving the Agricultural Transformation Agenda in Nigeria: Evidence from Climate Change Study in Southeast, Nigeria

CJ Obiora, MC Madukwe, EC Matthews-Njoku
2013 Journal of Agricultural Extension  
Majority (85%) of the respondents had no capability in terms of human resource development with regards to climate change.  ...  Also, bodies concerned should provide regular in-service training for respondents in other to promote human resource needed for tackling climate change issues.  ...  The paper therefore aims at: 1) determine the capabilities (in terms of machine/equipment acquisition and human resource development) of universities in tackling climate change; 2) identify factors that  ... 
doi:10.4314/jae.v17i1.13 fatcat:y7mr4j5bqzalhi5onq5banlbue

An Efficient Supervised Machine Learning Model Approach for Forecasting of Renewable Energy to Tackle Climate Change

Drumil Joshi et al., Drumil Joshi et al.,, TJPRC
2021 International Journal of Computer Science Engineering and Information Technology Research  
The primary use behind this data science and machine learning methodology, is to help judge the availability of renewable energy resources.  ...  It learns by carefully observing past patterns and their seasonality to make accurate predictions for the future.  ...  Learning Model Approach for Forecasting of Renewable Energy to Tackle Climate Change www The machine learning model is then trained using the training data.  ... 
doi:10.24247/ijcseitrjun20213 fatcat:kp4sdh7u65fmnixjkldzr5dxvu

Mining and Analysis of Air Quality Data to Aid Climate Change [chapter]

Lakshmi Babu Saheer, Mohamed Shahawy, Javad Zarrin
2020 IFIP Advances in Information and Communication Technology  
The data science and AI community has gathered around the world to support tackling the climate change problem in different domains.  ...  be built to extract strong evidences useful in building better policies around climate change.  ...  This research initiates the study of influence of AI on tackling climate change with regards to air quality data.  ... 
doi:10.1007/978-3-030-49190-1_21 fatcat:hqp6bht3wba5lh7knkrh4jlhjy

Capitalizing on AI?s Potential to Help Tackle the Climate Crisis [Opinion]

Sana Khareghani
2020 IEEE technology & society magazine  
While machine learning offers powerful solutions, there is still a need for a coordinated effort to identify how these tools -particularly next-gen tools -may best be applied to tackle climate change.  ...  s energy system will need to attract experts in machine learning.  ... 
doi:10.1109/mts.2020.2991499 fatcat:a3steekyojdahoohg6c6txz5li

AI Promises Climate-Friendly Materials

Gabriel Popkin
2021 Physics  
That rate is far too slow for confronting the climate crisis, which the world must begin tackling in earnest by the end of the decade, according to the Intergovernmental Panel on Climate Change (IPCC),  ...  T o tackle climate change, scientists and advocates have called for a bevy of actions that include reducing fossil fuel use, electrifying transportation, reforming agriculture, and mopping up excess carbon  ...  With the money spent and the labs built, it's time to prove the idea can deliver, he says. "The clock is ticking." Gabriel Popkin is a freelance science writer in Mount Rainier, Maryland.  ... 
doi:10.1103/physics.14.100 fatcat:j3evsn4btjcd7o2unajywsrlom

Advances in Hydrologic Forecasts and Water Resources Management

Fi-John Chang, Shenglian Guo
2020 Water  
The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modelling under changing  ...  The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention.  ...  Introduction Natural disasters have been inclined to increase and become more severe over the last decades due to climate change.  ... 
doi:10.3390/w12061819 fatcat:3d5ttx4z6jccdgehsfwv7g2biu

Rise of the machines

Navjoyt Ladher
2016 The BMJ (British Medical Journal)  
Against this backdrop, the UK Health Alliance on Climate Change launched its first report last week, calling for urgent action to tackle air pollution and climate change by reducing use of fossil fuels  ...  Could technology be the key to greater awareness about climate change?  ... 
doi:10.1136/bmj.i5777 fatcat:c33agb3opbatbhvgxno6fat3ma

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence [chapter]

Joseph Nyangon
2021 Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk  
This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies.  ...  The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget.  ...  Learning and Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.93488 Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence DOI: http://dx.doi.org  ... 
doi:10.5772/intechopen.93488 fatcat:7mvyre7z3vg3dmywnfaoe2fh7a
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