TY - JOUR
T1 - A mixed approach for urban flood prediction using Machine Learning and GIS
AU - Motta, Marcel
AU - Neto, Miguel de Castro
AU - Sarmento, Pedro
N1 - Motta, M., de Castro Neto, M., & Sarmento, P. (2021). A mixed approach for urban flood prediction using Machine Learning and GIS. International Journal of Disaster Risk Reduction, 56, 1-13. [102154]. https://doi.org/10.1016/j.ijdrr.2021.102154
PY - 2021/4/1
Y1 - 2021/4/1
N2 - Extreme weather conditions, as one of many effects of climate change, is expected to increase the magnitude and frequency of environmental disasters. In parallel, urban centres are also expected to grow significantly in the next years, making necessary to implement the adequate mechanisms to tackle such threats, more specifically flooding. This project aims to develop a flood prediction system using a combination of Machine Learning classifiers along with GIS techniques to be used as an effective tool for urban management and resilience planning. This approach can establish sensible factors and risk indices for the occurrence of floods at the city level, which could be instrumental for outlining a long-term strategy for Smart Cities. The most performant Machine Learning model was a Random Forest, with a Matthew's Correlation Coefficient of 0.77 and an Accuracy of 0.96. To support and extend the capabilities of the Machine Learning model, a GIS model was developed to find areas with higher likelihood of being flooded under critical weather conditions. Therefore, hot spots were defined for the entire city given the observed flood history. The scores obtained from the Random Forest model and the Hot Spot analysis were then combined to create a flood risk index.
AB - Extreme weather conditions, as one of many effects of climate change, is expected to increase the magnitude and frequency of environmental disasters. In parallel, urban centres are also expected to grow significantly in the next years, making necessary to implement the adequate mechanisms to tackle such threats, more specifically flooding. This project aims to develop a flood prediction system using a combination of Machine Learning classifiers along with GIS techniques to be used as an effective tool for urban management and resilience planning. This approach can establish sensible factors and risk indices for the occurrence of floods at the city level, which could be instrumental for outlining a long-term strategy for Smart Cities. The most performant Machine Learning model was a Random Forest, with a Matthew's Correlation Coefficient of 0.77 and an Accuracy of 0.96. To support and extend the capabilities of the Machine Learning model, a GIS model was developed to find areas with higher likelihood of being flooded under critical weather conditions. Therefore, hot spots were defined for the entire city given the observed flood history. The scores obtained from the Random Forest model and the Hot Spot analysis were then combined to create a flood risk index.
KW - Flood prediction
KW - GIS
KW - Machine learning
KW - Resilience planning
KW - Smart cities
UR - http://www.scopus.com/inward/record.url?scp=85101822911&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000636444100001
U2 - 10.1016/j.ijdrr.2021.102154
DO - 10.1016/j.ijdrr.2021.102154
M3 - Article
AN - SCOPUS:85101822911
SN - 2212-4209
VL - 56
SP - 1
EP - 13
JO - International Journal of Disaster Risk Reduction
JF - International Journal of Disaster Risk Reduction
M1 - 102154
ER -