Long-Term Hail Risk Assessment with Deep Neural Networks

Mozikov, Mikhail; Lukyanenko, Ivan; Makarov, Ilya; Bulkin, Alexander; Maximov, Yury

doi:10.1007/978-3-031-43085-5_23

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-43085-5_23

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Long-Term Hail Risk Assessment with Deep Neural Networks

Mikhail Mozikov,

Ivan Lukyanenko,

Ilya Makarov

et al.

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Case study on climate change effects and food security in Southeast Asia

Taniushkina,

Lukashevich,

Shevchenko

et al. 2024

Sci Rep

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Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

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Case study on climate change effects and food security in Southeast Asia

Taniushkina,

Lukashevich,

Shevchenko

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Long-Term Hail Risk Assessment with Deep Neural Networks

Cited by 1 publication

References 22 publications

Case study on climate change effects and food security in Southeast Asia

Case study on climate change effects and food security in Southeast Asia

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