scholarly journals Using machine learning to analyze physical causes of climate change: A case study of U.S. Midwest extreme precipitation

2021 ◽  
Author(s):  
Frances V. Davenport ◽  
Noah S. Diffenbaugh
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Extreme Precipitation

scholarly journals Will Climate Change Exacerbate the Economic Damage of Flood to Agricultural Production? A Case Study of Rice in Ha Tinh Province, Vietnam

2021 ◽  
Vol 9 ◽  
Author(s):  
Pham Quy Giang ◽  
Tran Trung Vy
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Agricultural Production ◽  
Climate Change Impacts ◽  
Economic Loss ◽  
Baseline Period ◽  
Rice Production ◽  
Generalized Extreme Value Distribution ◽  
Economic Damage

In developing countries in general and in Vietnam in particular, flood induced economic loss of agriculture is a serious concern since the livelihood of large populations depends on agricultural production. The objective of this study was to examine if climate change would exacerbate flood damage to agricultural production with a case study of rice production in Huong Son District of Ha Tinh Province, North-central Vietnam. The study applied a modeling approach for the prediction. Extreme precipitation and its return periods were calculated by the Generalized Extreme Value distribution method using historical daily observations and output of the MRI-CGCM3 climate model. The projected extreme precipitation data was then employed as an input of the Mike Flood model for flood modeling. Finally, an integrated approach employing flood depth and duration and crop calendar was used for the prediction of potential economic loss of rice production. Results of the study show that in comparison with the baseline period, an increase of 49.14% in the intensity of extreme precipitation was expected, while the frequency would increase 5 times by 2050s. As a result, the seriousness of floods would increase under climate change impacts as they would become more intensified, deeper and longer, and consequently the economic loss of rice production would increase significantly. While the level of peak flow was projected to rise nearly 1 m, leading the area of rice inundated to increase by 12.61%, the value of damage would rise by over 21% by 2050s compared to the baseline period. The findings of the present study are useful for long-term agricultural and infrastructural planning in order to tackle potential flooding threats to agricultural production under climate change impacts.

scholarly journals Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Geosciences ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 209
Author(s):  
Huiling Hu ◽  
Bilal M. Ayyub
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Extreme Precipitation ◽  
Circulation Model ◽  
Machine Learning Algorithms ◽  
Precipitation Intensity ◽  
Potential Threat ◽  
Drainage Systems ◽  
Idf Curves ◽  
Intensity Duration Frequency

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

scholarly journals Machine learning for research on climate change adaptation policy integration: an exploratory UK case study

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Robbert Biesbroek ◽  
Shashi Badloe ◽  
Ioannis N. Athanasiadis
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Climate Change Adaptation ◽  
Policy Research ◽  
Policy Integration ◽  
Adaptation Policy ◽  
Using Data ◽  
The Uk ◽  
Climate Change Adaptation Policy

Abstract Understanding how climate change adaptation is integrated into existing policy sectors and organizations is critical to ensure timely and effective climate actions across multiple levels and scales. Studying climate change adaptation policy has become increasingly difficult, particularly given the increasing volume of potentially relevant data available, the validity of existing methods handling large volumes of data, and comprehensiveness of assessing processes of integration across all sectors and public sector organizations over time. This article explores the use of machine learning to assist researchers when conducting adaptation policy research using text as data. We briefly introduce machine learning for text analysis, present the steps of training and testing a neural network model to classify policy texts using data from the UK, and demonstrate its usefulness with quantitative and qualitative illustrations. We conclude the article by reflecting on the merits and pitfalls of using machine learning in our case study and in general for researching climate change adaptation policy.

Understanding the Outcomes of Climate Change Campaigns in the Australian Environmental Movement

2019 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
Robyn Gulliver ◽  
Kelly S. Fielding ◽  
Winnifred Louis
Keyword(s):  
Climate Change ◽  
Collective Action ◽  
Environmental Advocacy ◽  
Personal Support ◽  
Governmental Organizations ◽  
Collective Response ◽  
Non Governmental Organizations ◽  
Advocacy Network ◽  
Australian Climate

Climate change is a global problem requiring a collective response. Grassroots advocacy has been an important element in propelling this collective response, often through the mechanism of campaigns. However, it is not clear whether the climate change campaigns organized by the environmental advocacy groups are successful in achieving their goals, nor the degree to which other benefits may accrue to groups who run them. To investigate this further, we report a case study of the Australian climate change advocacy sector. Three methods were used to gather data to inform this case study: content analysis of climate change organizations’ websites, analysis of website text relating to campaign outcomes, and interviews with climate change campaigners. Findings demonstrate that climate change advocacy is diverse and achieving substantial successes such as the development of climate change-related legislation and divestment commitments from a range of organizations. The data also highlights additional benefits of campaigning such as gaining access to political power and increasing groups’ financial and volunteer resources. The successful outcomes of campaigns were influenced by the ability of groups to sustain strong personal support networks, use skills and resources available across the wider environmental advocacy network, and form consensus around shared strategic values. Communicating the successes of climate change advocacy could help mobilize collective action to address climate change. As such, this case study of the Australian climate change movement is relevant for both academics focusing on social movements and collective action and advocacy-focused practitioners, philanthropists, and non-governmental organizations.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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