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Artificial Intelligence and Machine Learning (AI/ML) in Climate Change Impacts Analysis

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".

Deadline for manuscript submissions: closed (1 November 2023) | Viewed by 8569

Special Issue Editor

Dr. Rajib Maity

E-Mail Website
Guest Editor
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
Interests: hydroclimatology; climate change impacts assessment; bias correction; downscaling; analysis of hydroclimatic extremes; analysis of floods and droughts; sea level rise; statistical methods; probabilistic prediction and uncertainty quantification; remote sensing applications in hydrology; AI/ML in hydroclimatology

Special Issue Information

Dear Colleagues,

Artificial intelligence/Machine Learning (AI/ML) approaches are proven to have great potential in understanding many complex phenomena with a wide range of computational challenges. With the availability of a large set of observed and simulated records of hydrological, meteorological and climatological variables, AI/ML approaches have a huge potential scope in climate change impact analysis.

Towards this aim, several AI/ML approaches, such as Artificial Neural Network (ANN), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs), Deep Learning (DL), and Reinforcement Learning (RL), etc., have been found to yield undeniable performances and are now being widely applied by many researchers in many different parts of the globe.

Our interest lies primarily in the data-sparse regions across the world to showcase the potential of AI/ML approaches in climate change impact analysis.

The overall purpose of this Special Issue is to showcase the potential of AI/ML approaches in modelling different hydroclimatic processes all across the world.

We welcome original research articles and reviews for this Special Issue. Research areas may include (but are not limited to) the following:

i) Extreme events in a changing climate
ii) Flood and drought analyses
iii) Streamflow assessment
iv) Reservoir operation
v) Future climate and hydrology
vi) Change in precipitation
vii) Case studies on climate change impact assessment
viii) Mitigation strategies
ix) Policy making
x) Remote sensing applications
xi) Surface and ground water management under climate change

We look forward to receiving your contributions.

Dr. Rajib Maity
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate change
  • hydrology
  • meteorology
  • extreme events
  • artificial intelligence
  • machine learning
  • AI/ML

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Published Papers (4 papers)

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Research

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18 pages, 7274 KiB  
Article
Spatial Downscaling of ERA5 Reanalysis Air Temperature Data Based on Stacking Ensemble Learning
by Yuna Zhang, Jing Li and Deren Liu
Sustainability 2024, 16(5), 1934; https://doi.org/10.3390/su16051934 - 27 Feb 2024
Viewed by 1019
Abstract
High-resolution air temperature distribution data are of crucial significance for studying climate change and agriculture in the Yellow River Basin. Obtaining accurate and high-resolution air temperature data has been a persistent challenge in research. This study selected the Yellow River Basin as its [...] Read more.
High-resolution air temperature distribution data are of crucial significance for studying climate change and agriculture in the Yellow River Basin. Obtaining accurate and high-resolution air temperature data has been a persistent challenge in research. This study selected the Yellow River Basin as its research area and assessed multiple variables, including the land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope, aspect, longitude, and latitude. We constructed three downscaling models, namely, ET, XGBoost, and LightGBM, and applied a stacking ensemble learning algorithm to integrate these three models. Through this approach, ERA5-Land reanalysis air temperature data were successfully downscaled from a spatial resolution of 0.1° to 1 km, and the downscaled results were validated using observed data from meteorological stations. The results indicate that the stacking ensemble model significantly outperforms the three independent machine learning models. The integrated model, combined with the selected set of multiple variables, provides a feasible approach for downsizing ERA5 air temperature data. The stacking ensemble model not only effectively enhances the spatial resolution of ERA5 reanalysis air temperature data but also improves downscaled results to a certain extent. The downscaled air temperature data exhibit richer spatial texture information, better revealing spatial variations in air temperature within the same land class. This research outcome provides robust technical support for obtaining high-resolution air temperature data in meteorologically sparse or topographically complex regions, contributing significantly to climate, ecosystem, and sustainable development research. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning (AI/ML) in Climate Change Impacts Analysis)
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13 pages, 4283 KiB  
Article
The Climate of Innovation: AI’s Growing Influence in Weather Prediction Patents and Its Future Prospects
by Minjong Cheon and Changbae Mun
Sustainability 2023, 15(24), 16681; https://doi.org/10.3390/su152416681 - 8 Dec 2023
Viewed by 1490
Abstract
As the severity of climate change intensifies, understanding and predicting weather patterns have become paramount. Major firms worldwide have recognized this urgency, focusing their innovative efforts on weather prediction. In line with this trend, this research delves into the intricate patterns of patent [...] Read more.
As the severity of climate change intensifies, understanding and predicting weather patterns have become paramount. Major firms worldwide have recognized this urgency, focusing their innovative efforts on weather prediction. In line with this trend, this research delves into the intricate patterns of patent data within the realm of weather prediction from 2010 to 2023. The study unveils a standard timeline for patent grants in this domain, particularly noting a distinctive peak in grant durations between 1500 and 2000 days. The global landscape of weather prediction innovation is highlighted, pinpointing the United States, China, and Japan as pivotal contributors. A salient finding is the ascendant influence of artificial intelligence (AI) in this sector, underscored by the prevalence of AI-centric keywords such as “machine learning” and “neural network”. This trend exemplifies the ongoing paradigm shift toward data-driven methodologies in weather forecasting. A notable correlation was identified between patent trends and academic trends on platforms such as arXiv, especially concerning keywords such as “machine learning” and “deep learning”. Moreover, our findings indicate that the transformer network, given its rising prominence in deep learning realms, is predicted to be a future keyword trend in weather prediction patents. However, despite its insights, the study also grapples with limitations in its predictive modeling component, which aims at forecasting patent grant durations. Overall, this research offers a comprehensive understanding of the patent dynamics in weather prediction, illuminating the trajectory of technological advancements and the burgeoning role of AI. It holds implications for academia, industry, and policymaking in navigating the future of weather prediction technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning (AI/ML) in Climate Change Impacts Analysis)
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24 pages, 13412 KiB  
Article
General Circulation Model Downscaling Using Interpolation—Machine Learning Model Combination—Case Study: Thailand
by Chotirose Prathom and Paskorn Champrasert
Sustainability 2023, 15(12), 9668; https://doi.org/10.3390/su15129668 - 16 Jun 2023
Cited by 4 | Viewed by 1563
Abstract
Climate change, a global problem, is now impacting human life and nature in many sectors. To reduce the severity of the impacts, General Circulation Models (GCMs) are used for predicting future climate. The prediction output of a GCM requires a downscaling process to [...] Read more.
Climate change, a global problem, is now impacting human life and nature in many sectors. To reduce the severity of the impacts, General Circulation Models (GCMs) are used for predicting future climate. The prediction output of a GCM requires a downscaling process to increase its spatial resolution before projecting on local area. In order to downscale the output to a higher spatial resolution (less than 20 km), a statistical method is typically considered. By using this method, a large amount of historical observed data, up to 30 years, is essential. In some areas, the historical data is insufficient. Hence, the statistical method may not be suitable to downscale the output on the area which lacks the required data. Hence, this research aims to explore a high spatial resolution downscaling process that is able to provide a valid and high accuracy result in the Thailand area with a limitation in quantity of historical data. In this research, a combination of an interpolation and machine learning model called `IDW-ANN’ is proposed for downscaling the data under the condition. The prediction of temperature and precipitation from a GCM, IPSL-CM6A-LR in CMIP6 is downscaled by the proposed combination into a 1 km spatial resolution. After the performance evaluation, the IDW-ANN downscaling process showed good accuracy (RMSE, MAE, and R2) and valid downscaled results. The future climate situation in Thailand, in particular temperature, and precipitation level, in 2040 and 2100 under two scenarios of SSPs (SSP1-2.6 and SSP3-7.0) is also projected at 1 km resolution by using IDW-ANN. From the projection, the level of precipitation sums, and temperature seem to be increased in most of Thailand in all future scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning (AI/ML) in Climate Change Impacts Analysis)
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Review

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21 pages, 1670 KiB  
Review
Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development
by Aman Srivastava and Rajib Maity
Sustainability 2023, 15(23), 16461; https://doi.org/10.3390/su152316461 - 30 Nov 2023
Cited by 5 | Viewed by 3902
Abstract
This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. While much attention has been given to mitigation strategies, [...] Read more.
This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. While much attention has been given to mitigation strategies, this study uniquely delves into the AI–ML’s underexplored role in catalyzing climate change adaptation in contemporary and future urban centers. The research thoroughly explores diverse case studies from Africa, Asia, Australasia, Europe, North America, and South America, utilizing a methodological framework involving six-step and five-step models for systematic literature reviews. The findings underscore AI–ML achievements, illuminate challenges, and emphasize the need for context-specific and collaborative approaches. The findings imply that a one-size-fits-all approach is insufficient. Instead, successful adaptation strategies must be intricately linked to the particular characteristics, vulnerabilities, and intricacies of each region. Furthermore, the research underscores the importance of international collaboration, knowledge sharing, and technology transfer to expedite the integration of AI–ML into climate adaptation strategies globally. The study envisions a promising trajectory for AI–ML in the climate adaptation domain, emphasizing the necessity for ongoing research, innovation, and practical AI–ML applications. As climate change remains a defining challenge, this research predicts an increasingly pivotal role for AI–ML in constructing climate-resilient urban centers and promoting sustainable development. Continuous efforts to advance AI–ML technologies, establish robust policy frameworks, and ensure universal access are crucial for harnessing AI–ML’s transformative capabilities to combat climate change consequences. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning (AI/ML) in Climate Change Impacts Analysis)
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