Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions
Authors
Gutierrez Caloir, Beatriz EmmaAbebe, Yared Abayneh
Vojinovic, Zoran
Sanchez, Arlex
Mubeen, Adam
Ruangpan, Laddaporn
Manojlovic, Natasa
Plavšić, Jasna
Đorđević, Slobodan
Article (Published version)
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The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, and ecological dynamics. While there are some methods and tools for suitability mapping of small-scale NBSs, literature concerning the spatial allocation of large-scale NBSs is still lacking. The present work aims to develop new toolboxes and enhance an existing methodology by developing spatial analysis tools within a geographic information system (GIS) environment to allocate large-scale NBSs based on a multi-criteria algorithm. The methodologies combine machine learning spatial data processing techniques and hydrodynamic modelling for allocation of large-scale NBSs. The case studies concern selected areas in the Netherlands, Serbia, and Bolivia, focusing on three large-scale NBS: rainwater harvesting, wetland restoration, and natural riverbank stabilisation. Informa...tion available from the EC H2020 RECONECT project as well as other available data for the specific study areas was used. The research highlights the significance of incorporating machine learning, GIS, and remote sensing techniques for the suitable allocation of large-scale NBSs. The findings may offer new insights for decision-makers and other stakeholders involved in future sustainable environmental planning and climate change adaptation.
Keywords:
flood risk reduction / arge-scale nature-based solutions / machine learning / NBS planning / spatial data processingSource:
Blue-Green Systems, 2023, 5, 2, 186-Funding / projects:
- RECONECT- Regenarating ECOsystems with Nature-based solutions for hydro-meteorological risk rEduCTion (EU-H2020-776866)
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GraFarTY - JOUR AU - Gutierrez Caloir, Beatriz Emma AU - Abebe, Yared Abayneh AU - Vojinovic, Zoran AU - Sanchez, Arlex AU - Mubeen, Adam AU - Ruangpan, Laddaporn AU - Manojlovic, Natasa AU - Plavšić, Jasna AU - Đorđević, Slobodan PY - 2023 UR - https://grafar.grf.bg.ac.rs/handle/123456789/3350 AB - The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, and ecological dynamics. While there are some methods and tools for suitability mapping of small-scale NBSs, literature concerning the spatial allocation of large-scale NBSs is still lacking. The present work aims to develop new toolboxes and enhance an existing methodology by developing spatial analysis tools within a geographic information system (GIS) environment to allocate large-scale NBSs based on a multi-criteria algorithm. The methodologies combine machine learning spatial data processing techniques and hydrodynamic modelling for allocation of large-scale NBSs. The case studies concern selected areas in the Netherlands, Serbia, and Bolivia, focusing on three large-scale NBS: rainwater harvesting, wetland restoration, and natural riverbank stabilisation. Information available from the EC H2020 RECONECT project as well as other available data for the specific study areas was used. The research highlights the significance of incorporating machine learning, GIS, and remote sensing techniques for the suitable allocation of large-scale NBSs. The findings may offer new insights for decision-makers and other stakeholders involved in future sustainable environmental planning and climate change adaptation. T2 - Blue-Green Systems T1 - Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions IS - 2 SP - 186 VL - 5 DO - 10.2166/bgs.2023.040 ER -
@article{ author = "Gutierrez Caloir, Beatriz Emma and Abebe, Yared Abayneh and Vojinovic, Zoran and Sanchez, Arlex and Mubeen, Adam and Ruangpan, Laddaporn and Manojlovic, Natasa and Plavšić, Jasna and Đorđević, Slobodan", year = "2023", abstract = "The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, and ecological dynamics. While there are some methods and tools for suitability mapping of small-scale NBSs, literature concerning the spatial allocation of large-scale NBSs is still lacking. The present work aims to develop new toolboxes and enhance an existing methodology by developing spatial analysis tools within a geographic information system (GIS) environment to allocate large-scale NBSs based on a multi-criteria algorithm. The methodologies combine machine learning spatial data processing techniques and hydrodynamic modelling for allocation of large-scale NBSs. The case studies concern selected areas in the Netherlands, Serbia, and Bolivia, focusing on three large-scale NBS: rainwater harvesting, wetland restoration, and natural riverbank stabilisation. Information available from the EC H2020 RECONECT project as well as other available data for the specific study areas was used. The research highlights the significance of incorporating machine learning, GIS, and remote sensing techniques for the suitable allocation of large-scale NBSs. The findings may offer new insights for decision-makers and other stakeholders involved in future sustainable environmental planning and climate change adaptation.", journal = "Blue-Green Systems", title = "Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions", number = "2", pages = "186", volume = "5", doi = "10.2166/bgs.2023.040" }
Gutierrez Caloir, B. E., Abebe, Y. A., Vojinovic, Z., Sanchez, A., Mubeen, A., Ruangpan, L., Manojlovic, N., Plavšić, J.,& Đorđević, S.. (2023). Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions. in Blue-Green Systems, 5(2), 186. https://doi.org/10.2166/bgs.2023.040
Gutierrez Caloir BE, Abebe YA, Vojinovic Z, Sanchez A, Mubeen A, Ruangpan L, Manojlovic N, Plavšić J, Đorđević S. Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions. in Blue-Green Systems. 2023;5(2):186. doi:10.2166/bgs.2023.040 .
Gutierrez Caloir, Beatriz Emma, Abebe, Yared Abayneh, Vojinovic, Zoran, Sanchez, Arlex, Mubeen, Adam, Ruangpan, Laddaporn, Manojlovic, Natasa, Plavšić, Jasna, Đorđević, Slobodan, "Combining machine learning and spatial data processing techniques for allocation of large-scale nature-based solutions" in Blue-Green Systems, 5, no. 2 (2023):186, https://doi.org/10.2166/bgs.2023.040 . .