[HTML][HTML] Machine learning for spatial analyses in urban areas: a sco** review
The challenges for sustainable cities to protect the environment, ensure economic growth,
and maintain social justice have been widely recognized. Along with the digitization …
and maintain social justice have been widely recognized. Along with the digitization …
Data acquisition for urban building energy modeling: A review
Abstract Urban Building Energy Modeling (UBEM) is essential for urban energy-related
applications. Its generation mainly requires four data inputs, including geometric data, non …
applications. Its generation mainly requires four data inputs, including geometric data, non …
[HTML][HTML] Evaluating urban flood risk using hybrid method of TOPSIS and machine learning
With the growth of cities, urban flooding has increasingly become an issue for regional and
national governments. The destructive effects of floods are magnified in cities. Accurate …
national governments. The destructive effects of floods are magnified in cities. Accurate …
[HTML][HTML] Spatial flood susceptibility map** using an explainable artificial intelligence (XAI) model
Floods are natural hazards that lead to devastating financial losses and large displacements
of people. Flood susceptibility maps can improve mitigation measures according to the …
of people. Flood susceptibility maps can improve mitigation measures according to the …
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method
Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has
been among the most devastated regions affected by the major floods. While the temporal …
been among the most devastated regions affected by the major floods. While the temporal …
Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources
This paper reviews the most important information fusion data-driven algorithms based on
Machine Learning (ML) techniques for problems in Earth observation. Nowadays we …
Machine Learning (ML) techniques for problems in Earth observation. Nowadays we …
Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms
Flash flooding is considered one of the most dynamic natural disasters for which measures
need to be taken to minimize economic damages, adverse effects, and consequences by …
need to be taken to minimize economic damages, adverse effects, and consequences by …
Flood susceptibility map** through the GIS-AHP technique using the cloud
KC Swain, C Singha, L Nayak - ISPRS International Journal of Geo …, 2020 - mdpi.com
Flood susceptibility map** is essential for characterizing flood risk zones and for planning
mitigation approaches. Using a multi-criteria decision support system, this study investigated …
mitigation approaches. Using a multi-criteria decision support system, this study investigated …
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility map**
Flash flood is a typical natural hazard that occurs within a short time with high flow velocities
and is difficult to predict. In this study, we propose and validate a new soft computing …
and is difficult to predict. In this study, we propose and validate a new soft computing …
[HTML][HTML] Urban flood modeling using deep-learning approaches in Seoul, South Korea
Identification of flood-prone sites in urban environments is necessary, but there is insufficient
hydraulic information and time series data on surface runoff. To date, several attempts have …
hydraulic information and time series data on surface runoff. To date, several attempts have …