Chapter Machine learning for sustainable land management: A focus on Italy
Soil salinization poses a multifaceted challenge demanding a comprehensive approach combining environmental science, machine learning, geography, and socio- economic analysis. Our study integrates these disciplines to unravel the complexities of soil salinization and devise effective mitigation stra...
Sábháilte in:
| Príomhchruthaitheoirí: | , |
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| Formáid: | Online |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
Firenze University Press
2025
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20250801T173835_9791221505566_265 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869525105986502656 |
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| author | Matteo Dalle Vaglie Martellozzo, Federico |
| author_browse | Martellozzo, Federico Matteo Dalle Vaglie |
| author_facet | Matteo Dalle Vaglie Martellozzo, Federico |
| author_sort | Matteo Dalle Vaglie |
| collection | Directory of Open Access Books |
| description | Soil salinization poses a multifaceted challenge demanding a comprehensive approach combining environmental science, machine learning, geography, and socio- economic analysis. Our study integrates these disciplines to unravel the complexities of soil salinization and devise effective mitigation strategies. We ground our investigation in understanding the geological and climatic fundamentals governing soil properties and processes, with a focus on the Mediterranean coastal areas. By harnessing the power of machine learning, we navigate the high-dimensionality and non-linearity of soil salinization, incorporating a comprehensive set of variables spanning geological, climatic, human activity, and socio-economic dimensions. Our models, trained on extensive datasets, are robust and capable of capturing intricate patterns associated with soil salinization. The Mediterranean coastal areas, with their unique ecological, climatic, and anthropogenic interactions, serve as a valuable case study for exploring the dynamics of soil salinization. Our approach integrates data on historical geological changes with current climatic and anthropogenic variables, creating a comprehensive model that encapsulates the temporal and spatial dimensions of soil salinization. This study aims to contribute meaningfully to global efforts in sustainable land management and environmental preservation. |
| format | Online |
| id | doab-20.500.12854ir-163647 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1636472025-08-02T05:20:27Z Chapter Machine learning for sustainable land management: A focus on Italy Matteo Dalle Vaglie Martellozzo, Federico salinization land monitoring remote sensing soil management Soil salinization poses a multifaceted challenge demanding a comprehensive approach combining environmental science, machine learning, geography, and socio- economic analysis. Our study integrates these disciplines to unravel the complexities of soil salinization and devise effective mitigation strategies. We ground our investigation in understanding the geological and climatic fundamentals governing soil properties and processes, with a focus on the Mediterranean coastal areas. By harnessing the power of machine learning, we navigate the high-dimensionality and non-linearity of soil salinization, incorporating a comprehensive set of variables spanning geological, climatic, human activity, and socio-economic dimensions. Our models, trained on extensive datasets, are robust and capable of capturing intricate patterns associated with soil salinization. The Mediterranean coastal areas, with their unique ecological, climatic, and anthropogenic interactions, serve as a valuable case study for exploring the dynamics of soil salinization. Our approach integrates data on historical geological changes with current climatic and anthropogenic variables, creating a comprehensive model that encapsulates the temporal and spatial dimensions of soil salinization. This study aims to contribute meaningfully to global efforts in sustainable land management and environmental preservation. 2025-08-02T05:20:26Z 2025-08-02T05:20:26Z 2025-08-01T15:57:00Z 2024 chapter ONIX_20250801T173835_9791221505566_265 2975-0288 https://library.oapen.org/handle/20.500.12657/104815 9791221505566 https://directory.doabooks.org/handle/20.500.12854/163647 eng Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques open access image/jpeg Attribution-NonCommercial-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/104815/1/43706.pdf Firenze University Press 10.36253/979-12-215-0556-6.61 10.36253/979-12-215-0556-6.61 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221505566 12 Florence open access |
| spellingShingle | salinization land monitoring remote sensing soil management Matteo Dalle Vaglie Martellozzo, Federico Chapter Machine learning for sustainable land management: A focus on Italy |
| title | Chapter Machine learning for sustainable land management: A focus on Italy |
| title_full | Chapter Machine learning for sustainable land management: A focus on Italy |
| title_fullStr | Chapter Machine learning for sustainable land management: A focus on Italy |
| title_full_unstemmed | Chapter Machine learning for sustainable land management: A focus on Italy |
| title_short | Chapter Machine learning for sustainable land management: A focus on Italy |
| title_sort | chapter machine learning for sustainable land management a focus on italy |
| topic | salinization land monitoring remote sensing soil management |
| topic_facet | salinization land monitoring remote sensing soil management |
| url | ONIX_20250801T173835_9791221505566_265 |
| work_keys_str_mv | AT matteodallevaglie chaptermachinelearningforsustainablelandmanagementafocusonitaly AT martellozzofederico chaptermachinelearningforsustainablelandmanagementafocusonitaly |