A Novel Sample-Enhancement Framework for Machine Learning-Based Urban Flood Susceptibility Assessment DOI
Huabing Huang, Changpeng Wang,

Zhiwen Tao

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: unknown, P. 106314 - 106314

Published: Dec. 1, 2024

Language: Английский

Attribution analysis of urban social resilience differences under rainstorm disaster impact: Insights from interpretable spatial machine learning framework DOI

Tianshun Gu,

Hongbo Zhao, Yue Li

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106029 - 106029

Published: Dec. 1, 2024

Language: Английский

Citations

7
A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping DOI
Maelaynayn El Baida,

Mohamed Hosni,

Farid Boushaba

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(15), P. 5823 - 5864

Published: Aug. 3, 2024

Language: Английский

Citations

5
A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches DOI Creative Commons
Tania Islam, Ethiopia Bisrat Zeleke,

Mahmud Afroz

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 524 - 524

Published: Feb. 3, 2025

Climate change has led to an increase in global temperature and frequent intense precipitation, resulting a rise severe urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, overwhelmed drainage systems, particularly regions. As becomes more catastrophic causes significant environmental property damage, there urgent need understand address flood susceptibility mitigate future damage. review aims evaluate remote sensing datasets key parameters influencing provide comprehensive overview of the causative factors utilized mapping. also highlights evolution traditional, data-driven, big data, GISs (geographic information systems), machine learning approaches discusses advantages limitations different mapping approaches. By evaluating challenges associated with current practices, this paper offers insights into directions for improving management strategies. Understanding identifying foundation developing effective resilient practices will be beneficial mitigating

Language: Английский

Citations

0
Joint warning mechanism of urban flood considering comprehensive risk and emergency rescues DOI
Hongfa Wang,

Xinjian Guan,

Yu Meng

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105290 - 105290

Published: Feb. 1, 2025

Language: Английский

Citations

0
Forecasting rare hydrological events by machine learning methods: case study of ice jams on the Pechora river DOI

S. M. Iglin,

V. M. Moreido,

K. I. Golovnin

et al.

Lomonosov Geography Journal, Journal Year: 2025, Volume and Issue: 80(№1, 2025), P. 87 - 97

Published: Jan. 1, 2025

Rare hydrological events, as the name suggests, occur quite infrequently, but are often catastrophic for humans. They also inadequately provided with measurements (the so-called class imbalance). In its turn, this hinders creation of reliable models predicting such processes. This is especially evident when constructing natural processes using machine learning algorithms, which particularly sensitive to class-imbalanced samples. The study attempts overcome above-mentioned limitations by supplementing a series model training artificially generated events. subject and object were long-term forecasts ice jams occurring at mouth Pechora River in Arctic area European Russia. Data on collected over long period observations, applicable predictors selected. following algorithms used: k-nearest neighbors (KNN), logistic regression, gradient boosting (CatBoost), multilayer perceptron (MLP). As result all demonstrated higher quality modeling after artificial events series. confirms prospects method rarely

Language: Английский

Citations

0
Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator DOI
Hongfa Wang,

Xinjian Guan,

Yu Meng

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 111, P. 104753 - 104753

Published: Aug. 12, 2024

Language: Английский

Citations

2
Urban flood prediction based on PCSWMM and stacking integrated learning model DOI

Bingkun Du,

Min Wang,

Jinping Zhang

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 29, 2024

Language: Английский

Citations

2
A Novel Framework for Optimization and Evaluation of Sensors Network in Urban Drainage System DOI
Yue Zheng, Xiaoming Jin,

Jun Wei

et al.

Water Research, Journal Year: 2024, Volume and Issue: 270, P. 122833 - 122833

Published: Nov. 24, 2024

Language: Английский

Citations

1
A Novel Sample-Enhancement Framework for Machine Learning-Based Urban Flood Susceptibility Assessment DOI
Huabing Huang, Changpeng Wang,

Zhiwen Tao

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: unknown, P. 106314 - 106314

Published: Dec. 1, 2024

Language: Английский

Citations

0