Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships
The impact of climate change on hydrological processes, droughts, land use patterns, and ecosystem health is a critical area of research for understanding and managing the future of our planet. At the same time, changes in land use, agricultural methods, and population growth may contribute to clima...
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| 格式: | Online |
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| 语言: | 英语 |
| 出版: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| 在线阅读: | ONIX_20250812T110751_9783725841615_357 |
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| _version_ | 1869514958313619456 |
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| collection | Directory of Open Access Books |
| description | The impact of climate change on hydrological processes, droughts, land use patterns, and ecosystem health is a critical area of research for understanding and managing the future of our planet. At the same time, changes in land use, agricultural methods, and population growth may contribute to climate change. With its ability to process large datasets and identify hidden patterns, deep learning has provided new tools for analyzing complex environmental data and developing predictive models. These tools offer a promising avenue for advancing our potential response to environmental challenges. This Special Issue brings together researchers from diverse fields, applying deep learning methods and new models to investigate climate change drivers and their impact on droughts, desertification, ice sheet melting, land use changes, and ecosystem service value. The goal is to enhance the capacity for predicting the interrelationships between the above-mentioned environmental components. |
| format | Online |
| id | doab-20.500.12854ir-165602 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1656022025-08-12T09:59:17Z Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships Lavee, Hanoch Liu, Jinping deep learning techniques climate change impact carbon emission drought forecast desertification nitrate leaching ice sheet mass loss land use change population growth predictive environmental modeling remote sensing applications ecosystem service value thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities::KNBW Water industries thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology::TQK Pollution control The impact of climate change on hydrological processes, droughts, land use patterns, and ecosystem health is a critical area of research for understanding and managing the future of our planet. At the same time, changes in land use, agricultural methods, and population growth may contribute to climate change. With its ability to process large datasets and identify hidden patterns, deep learning has provided new tools for analyzing complex environmental data and developing predictive models. These tools offer a promising avenue for advancing our potential response to environmental challenges. This Special Issue brings together researchers from diverse fields, applying deep learning methods and new models to investigate climate change drivers and their impact on droughts, desertification, ice sheet melting, land use changes, and ecosystem service value. The goal is to enhance the capacity for predicting the interrelationships between the above-mentioned environmental components. 2025-08-12T09:59:15Z 2025-08-12T09:59:15Z 2025 book ONIX_20250812T110751_9783725841615_357 9783725841615 9783725841622 https://directory.doabooks.org/handle/20.500.12854/165602 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10991 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4162-2 10.3390/books978-3-7258-4162-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725841615 9783725841622 268 open access |
| spellingShingle | deep learning techniques climate change impact carbon emission drought forecast desertification nitrate leaching ice sheet mass loss land use change population growth predictive environmental modeling remote sensing applications ecosystem service value thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities::KNBW Water industries thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology::TQK Pollution control Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships |
| title | Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships |
| title_full | Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships |
| title_fullStr | Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships |
| title_full_unstemmed | Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships |
| title_short | Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships |
| title_sort | deep learning of climate change and extreme events hydrological processes and land use dynamics relationships |
| topic | deep learning techniques climate change impact carbon emission drought forecast desertification nitrate leaching ice sheet mass loss land use change population growth predictive environmental modeling remote sensing applications ecosystem service value thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities::KNBW Water industries thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology::TQK Pollution control |
| topic_facet | deep learning techniques climate change impact carbon emission drought forecast desertification nitrate leaching ice sheet mass loss land use change population growth predictive environmental modeling remote sensing applications ecosystem service value thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities::KNBW Water industries thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology::TQK Pollution control |
| url | ONIX_20250812T110751_9783725841615_357 |