[HTML][HTML] Machine learning for spatial analyses in urban areas: a sco** review

Y Casali, NY Aydin, T Comes - Sustainable cities and society, 2022 - Elsevier
The challenges for sustainable cities to protect the environment, ensure economic growth,
and maintain social justice have been widely recognized. Along with the digitization …

Data acquisition for urban building energy modeling: A review

C Wang, M Ferrando, F Causone, X **, X Zhou… - Building and …, 2022 - Elsevier
Abstract Urban Building Energy Modeling (UBEM) is essential for urban energy-related
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

E Rafiei-Sardooi, A Azareh, B Choubin… - International Journal of …, 2021 - Elsevier
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 …

[HTML][HTML] Spatial flood susceptibility map** using an explainable artificial intelligence (XAI) model

B Pradhan, S Lee, A Dikshit, H Kim - Geoscience Frontiers, 2023 - Elsevier
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 …

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

FS Hosseini, B Choubin, A Mosavi, N Nabipour… - Science of the total …, 2020 - Elsevier
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 …

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

S Salcedo-Sanz, P Ghamisi, M Piles, M Werner… - Information …, 2020 - Elsevier
This paper reviews the most important information fusion data-driven algorithms based on
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

SS Band, S Janizadeh, S Chandra Pal, A Saha… - Remote Sensing, 2020 - mdpi.com
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 …

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 …

A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility map**

DT Bui, PTT Ngo, TD Pham, A Jaafari, NQ Minh… - Catena, 2019 - Elsevier
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 …

[HTML][HTML] Urban flood modeling using deep-learning approaches in Seoul, South Korea

X Lei, W Chen, M Panahi, F Falah, O Rahmati… - Journal of …, 2021 - Elsevier
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 …