TY  - 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 . .

TY  - JOUR
AU  - Mubeen, Adam
AU  - Ruangpan, Laddaporn
AU  - Vojinovic, Zoran
AU  - Torres, Arlex Sanchez
AU  - Plavšić, Jasna
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2451
AB  - Adverse effects of climate change are increasing around the world and the floods are
posing significant challenges for water managers. With climate projections showing
increased risks of storms and extreme precipitation, the use of traditional measures alone
is no longer an option. Nature-Based Solutions (NBS) offer a suitable alternative to
reduce the risk of flooding and provide multiple benefits. However, planning such
interventions requires careful consideration of various factors and local contexts. The
present paper provides contribution in this direction and it proposes a methodology for
allocation of large-scale NBS using suitability mapping. The methodology was implemented
within the toolboxes of ESRI ArcMap software in order to map suitability for four
types of NBS interventions: floodplain restoration, detention basins, retention ponds, and
river widening. The toolboxes developed were applied to the case study area in Serbia,
i.e., the Tamnava River basin. Flood maps were used to determine the volume of
floodwater that needs to be stored for reducing flood risk in the basin and subsequent
downstream areas. The suitability maps produced indicate the potential of the new
methodology and its application as a decision-support tool for selection and allocation
of large-scale NBS.
T2  - Water Resources Management
T1  - Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction
EP  - 3081
SP  - 3063
VL  - 35
DO  - 10.1007/s11269-021-02848-w
ER  - 

@article{
author = "Mubeen, Adam and Ruangpan, Laddaporn and Vojinovic, Zoran and Torres, Arlex Sanchez and Plavšić, Jasna",
year = "2021",
abstract = "Adverse effects of climate change are increasing around the world and the floods are
posing significant challenges for water managers. With climate projections showing
increased risks of storms and extreme precipitation, the use of traditional measures alone
is no longer an option. Nature-Based Solutions (NBS) offer a suitable alternative to
reduce the risk of flooding and provide multiple benefits. However, planning such
interventions requires careful consideration of various factors and local contexts. The
present paper provides contribution in this direction and it proposes a methodology for
allocation of large-scale NBS using suitability mapping. The methodology was implemented
within the toolboxes of ESRI ArcMap software in order to map suitability for four
types of NBS interventions: floodplain restoration, detention basins, retention ponds, and
river widening. The toolboxes developed were applied to the case study area in Serbia,
i.e., the Tamnava River basin. Flood maps were used to determine the volume of
floodwater that needs to be stored for reducing flood risk in the basin and subsequent
downstream areas. The suitability maps produced indicate the potential of the new
methodology and its application as a decision-support tool for selection and allocation
of large-scale NBS.",
journal = "Water Resources Management",
title = "Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction",
pages = "3081-3063",
volume = "35",
doi = "10.1007/s11269-021-02848-w"
}

Mubeen, A., Ruangpan, L., Vojinovic, Z., Torres, A. S.,& Plavšić, J.. (2021). Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction. in Water Resources Management, 35, 3063-3081.
https://doi.org/10.1007/s11269-021-02848-w

Mubeen A, Ruangpan L, Vojinovic Z, Torres AS, Plavšić J. Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction. in Water Resources Management. 2021;35:3063-3081.
doi:10.1007/s11269-021-02848-w .

Mubeen, Adam, Ruangpan, Laddaporn, Vojinovic, Zoran, Torres, Arlex Sanchez, Plavšić, Jasna, "Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction" in Water Resources Management, 35 (2021):3063-3081,
https://doi.org/10.1007/s11269-021-02848-w . .