Edited by: Qin Xin, Ravi Samikannu, and Chuliang Wei
Climate change deals with the global phenomenon of climate transformation that significantly impacts the earth's usual climatic conditions (temperature, precipitation, wind, etc.). They are mainly caused due to human-made activities. Still, many people were unaware of the actual impacts of climate change, and they greatly enhance its risk through their day to day activities. The major source of climate change is global warming, which is primarily caused by the greenhouse effect. The emission of greenhouse gases from everyday human activities results in an unprecedented rise in earth temperature, and it is predicted to nurture even more in the future if left unaddressed. Rapid urbanization and industrial revolution are the other main causes that lead to the risk of climate change with increased energy demand and production, especially in the form of fossil fuels. The growing risk of climate change has a disastrous impact on earth organisms, including human beings and earth's flora and fauna. It further leads to the destruction of the food chain and economic resources. The impacts will be more crucial in developing and under-developed countries in comparison to the developed countries. While there exist possible literature to adapt to mitigate the impacts of climate change, these may become ineffective and obsolete over time as the severity may increase. Hence finding more advanced ways to predict, analyze, monitor, and mitigate climate change impacts has become crucially unavoidable.
Artificial Intelligence (AI) is a disruptive paradigm that has greater potential to assess, predict, and mitigate the risk of climate change with efficient use of data, learning algorithms, and sensing devices. It performs a calculation, makes predictions, and take decisions to mitigate the impacts of climate change. By developing effective models for weather forecasting and environmental monitoring, AI makes us better understand the impacts of climate change across various geographical locations. It interprets climatic data and predicts weather events, extreme climate conditions, and other socio-economic impacts of climate change and precipitation. From a technical perspective, AI offers better climatic predictions, shows the impacts of extreme weather, finds the actual source of carbon emitters and includes numerous other reasonable contributions. This enables the policymakers to be aware of the rising sea levels, earth hazards, hurricanes, temperature change, disruption to natural habitats, and species extinction. Nevertheless, the research community and experts have already started focusing on climate informatics with AI paradigms. The predictive models are more appropriate to short-term forecasting models and diverge from the long-term prediction, assessment, and mitigation. To get the maximum benefit from AI for climate change mitigation, more advanced researches are necessitated towards this domain.
This special issue invites researchers and data scientists to apply their knowledge and skillsets to explore data-driven solutions for climate change mitigation from the both commercial and scientific scale. Data acquired from industries such as food, energy, healthcare, and manufacturing is of prime importance to be analyzed for climate change mitigation.
Potential topics included, but not limited:
- AI assisted prediction models for climate change mitigation
- Role of machine vision in climate informatics and forecasting
- Recent trends in AI to reduce carbon footprints for a sustainable environment
- AI for earth hazard management
- AI to promote eco-friendly energy production and consumption
- AI assisted expert systems for climate change risk prediction and assessment
- AI assisted big data analytics Synergy of IoT, big data, cloud computing, and AI techniques in climate change prediction and mitigation
- Machine learning for sustainable green future
- AI in reducing the impacts of global warming
- Deep learning for sustainable earth surveillance and earth informatics
Important Dates:
Submission of manuscripts: 25 December 2021
Notification to authors: 28 February 2022
Revised version due date: 30 April 2022
Final decision: 30 August 2022
Guest Editor Details:
Dr. Qin Xin (Managing Guest Editor)
Professor,
Faculty of Science and Technology,
University of the Faroe Islands,
Faroe Islands, Denmark.
E-Mail: qinx@ieee.org
Dr. Ravi Samikannu
Associate Professor,
Department of Electrical Computer and Telecommunications Engineering,
Faculty of Engineering and Technology,
Botswana International University of Science and Technology,
Palapye, Botswana.
E-Mail: ravis@biust.ac.bw
Dr. Chuliang Wei
Associate Professor,
Department of Electronic Engineering,
Shantou University, Guangdong, China.
E-Mail: clwei@stu.edu.cn
Submission instructions
Before submitting your manuscript, please ensure you have carefully read the Submission Guidelines for Ecological Processes. The complete manuscript should be submitted through the Ecological Processes submission system. To ensure that you submit to the correct article collection please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the article collection on 'Artificial Intelligence for Climate Change Risk Prediction, Adaptation, & Mitigation'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
Articles in the special issue will be available on this page upon online.